Happiness in Everyday Life - The Doris A. Howell Foundation for

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MIHALY CSIKSZENTMIHALYI and JEREMY HUNTER
HAPPINESS IN EVERYDAY LIFE: THE USES OF
EXPERIENCE SAMPLING∗
(Received 29 January 2003; Accepted 17 February 2003)
ABSTRACT. This paper uses the Experience Sampling Method data drawn from a
national sample of American youth. It examines the proximal environmental factors
as well as behaviors and habits that correlate to personal happiness. Momentarylevel scores show that reported happiness varies significantly both by day of week
and time of day. Furthermore, particular activities are associated with varying degrees
of happiness. School activities rate below average scores in happiness, while social,
active and passive leisure activities are above average. Particular companions also
correlate to differing level of happiness. Being alone rates the lowest levels of happiness, while being with friend corresponds to the highest. Person-level averages of
happiness suggest that both higher social class and age correlate with lower levels
of happiness, while gender and race do not. Paradoxically, youth who spend more
time in school and social activities are happier than those who spend less. Unexpectedly, students who spend more time pleasure reading report lower levels of
happiness. Finally, feeling good about the self, excited, proud, sociable, active as
well as being in the conditions for flow experience are the strongest predictors of trait
happiness.
KEY WORDS: experience sampling, happiness, usual daily activities
Current understanding of human happiness points at five major effects
on this emotion. These are, moving from those most impervious to
change to those that are most under personal control: genetic determinants, macro-social conditions, chance events, proximal environment
and personality. It is not unlikely that, as behavioral geneticists insist, a
“set level” coded in our chromosomes accounts for perhaps as much as
half of the variance in self-reported happiness (Lykken and Tellegen,
1996; Tellegen et al., 1988). These effects are probably mediated by
temperamental traits like extraversion, which are partly genetically
determined and which are in turn linked to happiness (Myers, 1993).
Cross-national comparisons suggest that macro-social conditions such
as extreme poverty, war and social injustice are all obstacles to happiness (Inglehart and Klingemann, 2000; Veenhoven, 1995). Chance
events like personal tragedies, illness, or sudden strokes of good fortune may drastically affect the level of happiness, but apparently these
∗
This study was made possible by a grant from the Alfred P. Sloan
Foundation.
Journal of Happiness Studies 4: 185–199, 2003.
© 2003 Kluwer Academic Publishers. Printed in the Netherlands.
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MIHALY CSIKSZENTMIHALYI AND JEREMY HUNTER
effects do not last long (Brickman et al., 1978; Diener, 2000). One
might include under the heading of the proximal environment the social
class, community, family and economic situation – in other words, those
factors in the immediate surroundings that may have an impact on a
person’s well-being. And finally, habits and coping behaviors developed by the individual will have an important effect. Hope, optimism
and the ability to experience flow can be learned and thus moderate
one’s level of happiness (Csikszentmihalyi, 1997; Seligman, 2002).
In this paper, we present a method that allows investigators to study
the impact of momentary changes in the environment on people’s happiness levels, as well as its more lasting, trait-like correlates. Research
on happiness generally considers this emotion to be a personal trait.
The overall happiness level of individuals is measured by a survey
or questionnaire, and then “happy” people – those who score higher
on a one-time response scale – are contrasted with less happy ones.
Whatever distinguishes the two groups is then assumed to be a condition affecting happiness. This perspective is a logical outcome of the
methods used, namely, one-time measures. If a person’s happiness level
is measured only once, it is by definition impossible to detect intraindividual variations. Yet, we know quite well that emotional states,
including happiness, are quite volatile and responsive to environmental
conditions.
Of course both common sense and psychological research suggests
that when positive events happen in a person’s life, happiness increases.
For instance Schwartz and Strack (1999) have shown that even such
trivial events as one’s home team winning a soccer match, or the information that the weather in one’s hometown is better than the weather in
surrounding areas, will raise happiness levels. However, they warn that:
“. . . subjective well-being cannot be predicted on the basis of objective
circumstances, unless one takes the construal process into account”
(p. 61). In other words, the impact of external events on happiness is
mediated by the person’s system of values and cognitive interpretive
structures.
It is to detect variations in emotional states over time that the
Experience Sampling Method (ESM) was developed. This method
relies on subjects’ responses to an electronic pager that signals at random times during the waking hours of the day, yielding up to fifty
measures of happiness at specific moments during an average week.
Each time the pager signals, the respondents rate their experiential
states, including their levels of happiness (e.g. Csikszentmihalyi et al.,
HAPPINESS IN EVERYDAY LIFE
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1977; Kubey et al., 1996; Csikszentmihalyi and Schneider, 2001;
a handbook for using the ESM is in preparation, see Hektner, in press).
This method not only accounts for momentary states, but can also yield
trait-like measure by adding up for each person the separate momentary
responses.
Daniel Kahneman (1999) has described this approach as measuring
“point-instant utility”, and argued for its theoretical importance: “An
assessment of a person’s objective happiness over a period of time can
be derived from a dense record of the quality of experience at each
point” (p. 3).
Thus repeated measures taken over a representative segment of a
person’s life can be used in two ways: (a) as indicators of momentary happiness, which can help us understand the effect of Immediate
environmental circumstances; and (b) as personal traits derived from
aggregating the repeated responses over a week’s time, to derive a
trait-like measure of personal happiness.
The first comparison of state-like and trait-like characteristics of subjective experience using the ESM was a doctoral dissertation by Ronald
Graef (1978). In that work Graef found that while all the emotions were
more trait-determined than state-determined, this was particularly true
of happiness. In other words, a person’s average level of happiness
explained more of the variance in his or her responses over the week
than was explained by what that person was doing, where he or she
was, or whom he or she was with. This “set level” (cf. Tellegen et al.,
1988) explained about twice the variance in happiness compared to
other mood states. Longitudinal studies suggest a somewhat different
conclusion. In a 2-year follow-up of 455 high school students, the average ESM happiness scores correlated 0.55, more or less at the same
level as other mood variables. But a 4-year follow-up of a subset of
187 of these students showed only a correlation of 0.22 for happiness,
while r’s for all the other variables ranged from 0.34 (being in control)
to 0.56 (being relaxed), suggesting that self-reported happiness is less
stable than other dimensions of experience (Moneta et al., 2001; Patton,
1998; Hektner, in press).
In any case, there is obviously a great deal of variance unexplained by
a “set level” of happiness. In this paper we are going to use ESM data on
a group of over 800 adolescents to explicate the contributions of some of
the momentary conditions on intra-individual reports of happiness, and
then look from a trait-like perspective at how demographic variables
and patterns of behavior relate to over-all levels of happiness.
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METHOD
The Participants
The participants of this study are primary school students from the
Alfred P. Sloan Study of Youth and Social Development, a national
multi-year study involving 6th, 8th, 10th and 12th graders from
33 elementary and secondary schools from 12 communities across the
country. These sites were chosen to create a nationally-representative
sample based on the variation in labor force composition and participation, ethnicity, urbanicity, geographic location, and student ability
(see Csikszentmihalyi and Schneider, 2000 for a fuller description).
The 828 students included here are part of a focal group of 1215 youth.
The group here represents those who provided the minimum amount
of Experience Sampling Data and include 342 males (41.3%) and 486
females (58.7%), 491 Whites (59.3%), 54 Asians (6.5%), 131 Latinos
(15.8%), 145 African Americans (17.5%) and a small number (7) of
Native Americans (0.8%). Two-hundred and thirty-three, 6th graders
represented 28.1% of the sample, while the remainder were 236 Eighth
graders (28.5%), 196 Tenth graders (23.7%) and 163 Twelth graders
(19.7%). Social Class was measured on the community-level (rather
than through household income) and consisted of 118 students (14.4%)
from Poor communities, 133 (16.2%) from Working, 271 (33%) from
Middle, 212 (25.8%) from Upper Middle and 87 (10.6%) Upper classes.
Measures
Measures of subjective experience and time use are drawn from the
ESM, where each participant was given a programmable wristwatch
set to signal at random moments eight times a day from 7:30 am to
10:30 pm for one week. Upon hearing the signal, participants completed
a form containing open-ended questions about what they were doing
at that moment as well as multiple-choice items regarding whom they
were with and close-ended scales addressing a wide range of feelings
and conditions associated with that moment. The data included here
are from those students who completed at least 15 responses over the
course of the week.
The open-ended items about the student’s current activity were coded
into several dozen specific categories, that can also be converted into
much more generalized groupings such as School (eg. studying, listening to lecture), Active Leisure (playing games, sports), Passive Leisure
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(watching tv, listening to music), Maintenance (grooming, eating, transportation) and Work activities (after school jobs). In addition, two variables used for assessing the activity’s conditions for flow experiences
are (1) the amount to which they found the current activity Challenging
(a 1–9 scale, where 1 is the lowest and 9 the highest value) and (2) the
student’s level of Skill in the activity (using the same 1–9 scheme).
Mood variables include a 1–7 scale (1 being the most negative and 7,
the most positive value) asking the student if they felt Happy (vs. Sad),
Strong (vs. Weak), Proud (vs. Ashamed), Sociable (vs. Lonely), Excited
(vs. Bored), Active (vs. Passive) and a 1–10 scale (where 1 is the most
negative and 10, the most positive) asking “Did you feel good about
yourself?”. These variables can be used to refer to specific moments in
time, for example what is the level of happiness when watching television versus doing sports? Furthermore, an individual’s total responses
can also be combined to form a Person-level variable. Such variables
can be used to compare people who rank happier than others overall.
A third way these variables can be used is to combine the contextual and
the personal. For example, using happiness as referent, a Person-Level
contextual variable tells the amount of happiness a particular individual
experiences in a specific activity.
Momentary Changes in Happiness
Days of the Week
There is a widely held belief that people are more sad on certain days
of the week than on others. “Blue Mondays” in particular are held to
be depressing. In this sample variation in happiness (using “z” scores
calibrated on individual means) was very slight, although significant.
An ANOVA produced an F value of 3.4 (p < 0.002). The lowest
happiness was reported on Sundays, and each day afterwards happiness
increased slightly, reaching its peak on Saturdays (see Figure 1).
Post-hoc Bonferroni tests indicated that respondents were significantly happier on Saturdays than they were on Mondays, Tuesdays and
Wednesdays (Sunday responses were fewer and had a greater variance
in happiness, thus yielded no significant differences).
Clearly, the social structure of time has an impact on happiness: The
early part of the weekend, with its freedom from work or school, is
experienced as liberating. The effect is probably greater on adults, for
whom the working week is presumably even more constraining than it
is for teenagers.
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Figure 1. Happiness (beep-level z-score) by day of week.
Figure 2. Happiness (beep-level z-score) throughout the weekday.
Times of Day
During the weekdays, time is structured by work or school requirements
according to a circadian pattern. The first part of the day, spent at work
or school, tends to be less happy, except for a peak at lunch-time.
There is a dip after lunch, followed by higher reports of happiness in
the afternoon when one is again free (see Figure 2).
If we contrast afternoon reports with those obtained before noon, the
difference in happiness is striking (F = 56.5, p < 0.00001).
Activities
What one happens to be doing at the moment of the signal has an
even more specific effect on happiness. There are ten main activities
that teenagers do during the week, each taking up 2% or more of their
waking time. For seven of these ten, the average level of happiness is
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TABLE I
Happiness (aggregated Person–Level z-score) by top ten most frequent
activities∗
TV
Talking with Friends
Eating a Meal
Unspecified Homework
Individual Work
Listening to Lecture
Chores
Fun Reading/Writing
Mathematics
Talking with Family
Happy (z-score)
T-value
P<
N
0.03
0.35
0.19
−0.30
−0.11
−0.21
−0.21
−0.01
−0.25
−0.03
1.24
9.87
5.78
−8.21
−2.99
−5.36
−4.44
−0.14
−5.27
−0.53
NS
0.000
0.000
0.000
0.003
0.000
0.000
NS
0.000
NS
666
325
524
409
358
381
343
324
327
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∗ Activities
representing at least 2% of time during the week (1% is roughly
equal to 1 h)
significantly higher or lower than it is on the average (see Table I).
The highest level of happiness is reported when talking with friends
(Mean z = 0.35, t = 9.87, p < 0.00001), and the lowest when doing
school-related homework (Mean z = −0.30, t = −8.21, p < 0.00001).
Another way to observe the effects of activities is by combining them
into six major categories, which together account for 21,631 responses,
or 93% of the total. Four of the six categories are significantly different
from the average (p < 0.00001). Whenever students are involved with
School-related activities, their happiness level is below average (Mean
z = −0.19); when Socializing with friends, when involved in Active
Leisure, or in Passive Leisure it is above average (Mean z = 0.28,
0.19 and 0.11, respectively). Some of the happiest experiences reported
in the Active Leisure category are Sports (Mean z = 0.50), Music
(z = 0.29) and visual Art (z = 0.27). The other two major categories,
which are indistinguishable from the average in terms of happiness,
are Working and Maintenance activities such as doing chores, eating,
dressing, and so on.
Companions
Who one happens to be with companions, it also impacts significantly on the level of reported happiness. In terms of companionship,
youth experience the lowest levels of happiness when they are Alone
(Mean z = −0.12, p < 0.0001), with Teachers (Mean z = −0.09,
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p < 0.0001), and with Classmates (Mean z = −0.07, p < 0.0001)
while being with friends corresponds to the highest level (Mean
z = 0.21, p < 0.0001). Being with Parents is at the average for happiness, which is lower than being with a Sibling (Mean z = 0.03,
p < 0.016). Spending time with a Relative, however, is associated with
more happiness (Mean z = 0.09, p < 0.002) than either of these two
familial groups.
Person-Level Correlates of Happiness
The analysis thus far focused on how happiness is experienced at the
moment – how situational context relates to shifting levels of happiness
within the individual. The ESM data can be also analyzed at the person
level, making it possible to answer the question, what differentiates
young people who on the average report higher levels of happiness
from those who during the week report being less happy?
Demographic Characteristics
General traits of the person have rather strong relationships to happiness. The largest difference reflects the Social Class of Community
(SCC) in which the teenagers live. SCC was computed on five levels
of increasing affluence: Poor (mostly single-parent, unemployed),
Working Class, Middle Class, Upper-Middle Class and Upper Class.
Contrary to expectations, the highest level of happiness was reported
by young people living in Working Class communities, then by those
in Middle Class, Poor, Upper Class and finally Upper Middle Class
environments. An ANOVA in which all the demographic variables
(i.e. age, gender, SCC, Ethnic background) were entered showed the
strongest effect for SCC (F = 8.09, p < 0.0001).
Age was the second most important factor (F = 6.45, p < 0.0001)
Happiness decreases through the teenage years; it reaches its lowest
point by age 16, and then shows a small recovery by age 18 (see also
Moneta, 2001). Gender and Ethnic background did not show significant
effects, even though African-American and Hispanic youth had higher
levels of happiness than Caucasians and Asians – but these differences
appear to be due more to social class than to ethnicity.
Boys and girls generally did not differ in terms of happiness. However, the ANOVA showed one significant interaction (F = 2.92,
p < 0.02) between gender and SCC. Poor girls (5.5) experience more
happiness than Poor boys (5.0) (t = −2.51, p < 0.014).
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Activities
In the previous section, we have seen that teenagers are happier when
they do certain things (e.g. in leisure) than when they do others (e.g.
study). Here we are looking at the issue from a trait-like, rather than
a state-like perspective: in other words, are teenagers who spend more
time in leisure activities during the week happier than those who spend
more time studying?
Contrary to what one might expect, the amount of time spent in
school-related activities during the week is positively related to happiness (multiple regression (MR), t = 2.25, p < 0.024), indicating that
those teenagers who study more are in fact happier, even though studying is lower in happiness than most other activities. This apparently
paradoxical finding is one of the important ways in which the ESM can
reveal the fact that relationships that are negative at the state level can
at time be positive at the trait level. The percent of time students spend
socializing is also positively related to happiness (t = 2.61, p < 0.009).
In this case, both momentary and Person–Level relationships point in
the same direction. Young people feel happier when they interact with
peers, and those who do so more often are on the average happier than
those who interact less.
One unexpected finding was that of the smaller activity categories
the one that showed the strongest relation to happiness at the person
level was Reading a book for pleasure. The relationship was negative
(t = −2.09, p < 0.04), suggesting that teenagers who spend more
time during the week are also generally less happy. This result could
be due to the fact that young people who read more are less often in the
company of their peers. There is a slight negative correlation (−0.09,
p < 0.08, n = 825) between the amount of time spent reading and the
percent of time spent with friends.
Companions
The social context affects happiness in complex ways. Those young
people who spend more time alone are in general less happy (MR,
t = −3.85, p < 0.0001). Those who spend more time with relatives
during the week tend to be happier (MR, t = 2.24, p < 0.01). Although
being with friends is related to happiness it is not significantly so,
because older teenagers spend more time with friends, while being
less happy than younger ones. Therefore, the age effect cancels out the
beneficial effect of spending time with friends.
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TABLE II
Standardized regression coefficients from multiple stepwise regression of mean (Person–Level) happiness on Person–Level mood
variables (controlling for demographic variables)
Independent variables
Mean (Person–Level)
happiness
T-score
P<
Strong
Feel Good About Self
Sociable
Excited
Proud
Active
Grade Level in School
Constant
0.099
0.093
0.160
0.230
0.230
−0.050
−0.050
1.650
2.5
6.62
4.5
11.74
5.75
3
−5.06
9.2
0.012
0.000
0.000
0.000
0.000
0.003
0.000
0.000
Adjusted R 2
F -value
0.540
135.3
0.000
The Relation of Happiness to Other Moods
What other dimensions of subjective experience differentiate a happy
young person from one who is less so? To answer that question, we did
a regression in which the dependent variable was a person’s average
happiness score for the week, and the predictors included all the other
mood variables. Such a MR explained 55% of the variance in happiness
(Table II).
The strongest predictor of trait happiness was how Excited
(vs. Bored) a person felt, followed by the variables Feeling Good about
Self, Proud (vs. Ashamed), Sociable (vs. Lonely), feeling Active and
Strong (vs. Weak). The correlation coefficients of these variables with
Happiness (and controlling for age), were 0.58, 0.59, 0.47 and 0.53,
respectively (with N = 799, all p < 0.0001).
Happiness and the Conditions for Flow
It was expected that young people who spend more time in situations
they perceive as being conducive to flow would be on the whole happier.
To measure whether a person was more likely to be in a Flow condition
we calculated the percent of time spent in situations that were above
the mean level of challenge and the mean level of skill at the same time.
When a person was above the mean of skills but below mean challenge,
the condition was considered conducive to Relaxation. High challenges
and low skills were counted as Anxiety, and low challenges with low
skills as Apathy.
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TABLE III
Standardized regression coefficients from multiple stepwise regression
of mean (Person–Level) happiness on flow conditions (controlling for
demographic variables)
happiness
T-score
P<
Flow Condition
Relaxation Condition
Social Class of Community
Constant
0.013
0.008
−0.097
−0.080
5.560
6.05
4.93
−7.01
−3.1
31
0.000
0.000
0.000
0.002
0.000
Adjusted R 2
F -value
0.124
29.56
0.000
Table III shows the final regression model, which includes Age and
the Gender by SCC interaction as well as the four Flow-related variables. The full model explains 12.4% of the variance in happiness. The
frequency of time spent in the Flow condition is a very strong predictor
of happiness (t = 6.05, p < 0.0001) even after taking the significant
demographic variables into account.
The Final Model
To see if combining all the correlates of happiness in one model would
enhance understanding of the phenomenon, we created a final regression model that included the most promising variables form previous
analyses – excluding, however, the mood variables which as we have
seen above (Table II), explain 54% of the variance in happiness.
The resulting model is the one reported in Table IV. The combined
predictive value is not much higher than that of some of the demographic variables taken singly, as it attains only 15% of the variance in
happiness. Nevertheless, the pattern is suggestive.
The pattern can be summarized as follows: Happier teenagers tend
to be younger, from lower socio-economic circumstances. They spend
less time alone and less time reading books. They spend more time
either in high challenge/high skill Flow producing situations, or low
challenge/high skill Relaxing situations. These are also the young people who feel more Excited, Proud, Sociable, Strong, Active and Good
about themselves.
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TABLE IV
Standardized regression coefficients from multiple stepwise regression
of mean (Person–Level) happiness on flow conditions and time usage
(controlling for demographic variables)
happiness
Percent of Time
Spent Alone
In Flow Condition
In Relaxation Condition
Spent Reading/Writing for Fun
Social Class of Community
Constant
−0.010
0.013
0.009
−0.014
−0.080
−0.065
5.590
Adjusted R 2
F -value
0.150
24.77
T-score
−4.60
6.2
5.22
−2.08
−5.8
−2.5
31.46
P<
0.000
0.000
0.000
0.037
0.012
0.000
0.000
0.000
DISCUSSION
The ESM makes it possible to separate the immediate context of
happiness from more long-term conditions. In terms of momentary
effects, it is clear that what one does and whom one is with will modify
a person’s base-line of happiness. Freely chosen activities and the company of peers raise the level of happiness, while obligatory activities
like schoolwork and the condition of solitude lowers it. The social structure of time affects happiness in a similar way: young people are much
happier in the afternoons and evenings of weekdays, when they are
free of requirements imposed by adults, and on weekends. But by the
end of the weekend, on Sunday afternoons, their happiness decreases
in anticipation of the school-day to come.
The demographic analyses provide rather counterintuitive suggestions. That happiness decreases during the conflicted teenage years is
not surprising, and the recovery around age 18 has been documented
before (Moneta, 2001). What is surprising is the lack of positive correlation between happiness and financial affluence. That teenagers from
working-class, and even impoverished backgrounds should be happier
than upper-middle-class teenagers living in exclusive suburban communities is difficult to explain. It is possible that some selection bias
is responsible for this result: perhaps relatively more students from
lower class backgrounds who were happy volunteered and completed
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the ESM compared with more affluent students. But the rates of volunteering had been high in all schools, including the ones in the inner city
neighborhoods, so this explanation could not account entirely for the
findings. Perhaps in the affluent suburban sub-culture it is not “cool”
to admit to being happy. Or perhaps material well-being is in fact an
obstacle to happiness. Recent research on materialism suggests that
excessive concern with consumer goods and material possessions is
inversely related with positive developmental outcomes (Schmuck and
Sheldon, 2001). In any case, this finding clearly deserves further study.
Aggregating responses over a week’s time suggests that happiness
is strongly related to an extraverted lifestyle. Not being alone, feeling
excited, proud, being in high-challenge, high skill situations are all
related to how happy a young person feels. It seems that at least at
this stage of life an experience of what we may call “vitality,” or eros,
is the most distinctive feature of happiness (Csikszentmihalyi, 1990;
1999; see also Ryan and Frederick, 1997, for recent studies dealing
with vitality).
At the same time, it is important to notice that studying, which produces an experience of sadness as it is occurring, helps young people
feel happier in the long run. This is an example of how building “ psychological capital” involves the transformation of potentially negative
experiences in positive experience over time (Csikszentmihalyi, 2003).
For example, in a longitudinal study of talented teenagers we found that
only those who learned to enjoy practicing their talent (i.e. mathematics, music, science, art, athletics) were able to continue developing it
through the high school years. Those who became bored or stressed
when working on their talent sooner or later gave up, while those
who experienced flow in their work continued to perfect their talent
(Csikszentmihalyi et al., 1997).
These results suggest that momentary happiness, at least for young
people, is a function of the ability to express their potential vitality
as fully as it is possible given the socialization demands the adult
world places on them. Teenagers ascribe “happiness” to their moods
when they are in situations of relative freedom, in the company of
age-mates, able to engage in flow activities that stretch their skills and
makes them feel alive and proud. The same conditions are implicated
in more enduring, trait-like happiness. Here, however, happiness is
also affected by preparation for the future: young people who study
more are on the whole happier, presumably because they realize that
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by building psychological capital the range of opportunities and hence
their freedom will increase in the future.
If this is the case, the results have important implications for education and social policy. Happiness will increase to the extent that
individuals are provided with the means to learn skills that can be
deployed to meet reasonable challenges; that they are given freedom
to express themselves within bounds of responsibility; that they are
allowed to experience the joy of interaction with peers of one’s choice
and with adults that care for their well-being. These requirements for
happiness presumably operate at every level of societal complexity,
from the macro-level of the economy and political structure to the
meso- and micro-levels of community, school and family. There are
clear trends in contemporary life that militate against such conditions.
It is difficult for a young person to be happy when living in a sterile suburb that lacks opportunities for action, forced to attend schools where
there is little chance to express oneself except in abstract intellectual
terms, surrounded by a small nuclear family that is seldom together
and relaxed enough to interact freely. Understanding more clearly the
conditions that affect happiness is a prerequisite if social scientists are
to help improve the quality of life.
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Address for Correspondence:
MIHALY CSIKSZENTMIHALYI
Claremont Graduate University
The Quality of Life Research Center
171 E, Tenth Street, CA 91711
Claremont, USA
Journal of Personality and Social Psychology
2008, Vol. 95, No. 5, 1045–1062
Copyright 2008 by the American Psychological Association
0022-3514/08/$12.00 DOI: 10.1037/a0013262
Open Hearts Build Lives: Positive Emotions, Induced Through
Loving-Kindness Meditation, Build Consequential Personal Resources
Barbara L. Fredrickson
Michael A. Cohn
University of North Carolina at Chapel Hill
University of Michigan
Kimberly A. Coffey and Jolynn Pek
Sandra M. Finkel
University of North Carolina at Chapel Hill
B. L. Fredrickson’s (1998, 2001) broaden-and-build theory of positive emotions asserts that people’s
daily experiences of positive emotions compound over time to build a variety of consequential personal
resources. The authors tested this build hypothesis in a field experiment with working adults (n ⫽ 139),
half of whom were randomly-assigned to begin a practice of loving-kindness meditation. Results showed
that this meditation practice produced increases over time in daily experiences of positive emotions,
which, in turn, produced increases in a wide range of personal resources (e.g., increased mindfulness,
purpose in life, social support, decreased illness symptoms). In turn, these increments in personal
resources predicted increased life satisfaction and reduced depressive symptoms. Discussion centers on
how positive emotions are the mechanism of change for the type of mind-training practice studied here
and how loving-kindness meditation is an intervention strategy that produces positive emotions in a way
that outpaces the hedonic treadmill effect.
Keywords: emotions, meditation, positive psychology, broaden-and-build, mindfulness
marital satisfaction (Harker & Keltner, 2001), higher incomes
(Diener, Nickerson, Lucus, & Sandvik, 2002), and better physical
health (Doyle, Gentile, & Cohen, 2006; Richman et al., 2005).
People who experience frequent positive emotions have even been
shown to live longer (Danner, Snowdon, & Friesen, 2001; Moskowitz, 2003; Ostir, Markides, Black, & Goodwin, 2000). Indeed,
a recent meta-analysis of nearly 300 findings concluded that positive emotions produce success and health as much as they reflect
these good outcomes (Lyubomirsky, King, & Diener, 2005).
How do they do this? How do people’s fleeting and subtle
pleasant states pave the way to their later success, health, and
longevity? Fredrickson’s (1998) broaden-and-build theory of positive emotions outlines a possible path: Because positive emotions
arise in response to diffuse opportunities, rather than narrowlyfocused threats, positive emotions momentarily broaden people’s
attention and thinking, enabling them to draw on higher-level
connections and a wider-than-usual range of percepts or ideas. In
turn, these broadened outlooks often help people to discover and
build consequential personal resources. These resources can be
cognitive, like the ability to mindfully attend to the present moment; psychological, like the ability to maintain a sense of mastery
over environmental challenges; social, like the ability to give and
receive emotional support; or physical, like the ability to ward off
the common cold. People with these resources are more likely to
effectively meet life’s challenges and take advantage of its opportunities, becoming successful, healthy, and happy in the months and
years to come. Thus, the personal resources accrued, often unintentionally, through frequent experiences of positive emotions are posited to be keys to later increases in well-being. Put simply, the
broaden-and-build theory states that positive emotions widen people’s
outlooks in ways that, little by little, reshape who they are.
A paradox surrounds positive emotions. On one hand, they are
fleeting: Like any emotional state, feelings of joy, gratitude, interest, and contentment typically last only a matter of minutes.
Moreover, positive emotions are less intense and less attentiongrabbing than negative emotions (Baumeister, Bratslavsky,
Finkenauer, & Vohs, 2001) and are more diffuse (Ellsworth &
Smith, 1988). Yet on the other hand, research indicates that positive emotions contribute to important downstream life outcomes,
including friendship development (Waugh & Fredrickson, 2006),
Barbara L. Fredrickson, Kimberly A. Coffey, and Jolynn Pek, Department of Psychology, University of North Carolina at Chapel Hill; Michael
A. Cohn, Department of Psychology, University of Michigan; Sandra M.
Finkel, Preventive Cardiology Services, University of Michigan.
This work was supported by National Institute of Mental Health Grant
MH59615 to Barbara L. Fredrickson, with additional financial and procedural support from the Compuware Corporation (Detroit, Michigan). We
thank the leaders at Compuware who opened their minds and doors to the
study reported here and offer special thanks to the Compuware employees
who devoted their time and energy across months to participate in this
project. We acknowledge the contributions of Li Cai, Daniel Serrano, and
Patrick Curran in guiding us safely through new statistical terrain. Thanks
also go to Benjamin Figa for overseeing data collection; Jordana Adler and
Sid Tsai for assistance with data management and coding; and Tracey
Callison, Lahnna Catalino, and Bethany Kok for their comments. Finally,
Sandra M. Finkel wishes to acknowledge Ngawang Gehlek Rinpoche, her
personal mentor in loving-kindness.
Correspondence concerning this article should be addressed to Barbara
L. Fredrickson, Department of Psychology, Davie Hall, CB 3270, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599. E-mail:
[email protected]
1045
1046
FREDRICKSON, COHN, COFFEY, PEK, AND FINKEL
The key hypotheses of the broaden-and-build theory have received
empirical support from multiple laboratories. First, the broaden hypothesis holds that positive emotions broaden people’s attention and
thinking. Experiments have shown that, relative to neutral and negative states, induced positive emotions widen the scope of people’s
visual attention (Fredrickson & Branigan, 2005; Rowe, Hirsh, &
Anderson, 2007; Wadlinger & Isaacowitz, 2006), broaden their repertoires of desired actions (Fredrickson & Branigan, 2005), and increase their openness to new experiences (Kahn & Isen, 1993) and
critical feedback (Raghunathan & Trope, 2002). At the interpersonal
level, induced positive emotions increase people’s sense of “oneness”
with close others (Hejmadi, Waugh, Otake, & Fredrickson, 2008),
their trust in acquaintances (Dunn & Schweitzer, 2005), and their
ability to accurately recognize individuals of another race (Johnson &
Fredrickson, 2005). The empirical evidence is mounting, then, that
positive emotions broaden people’s attention and thinking in both
personal and interpersonal domains.
The second part of the theory, the build hypothesis, holds that
positive emotions set people on trajectories of growth that, over
time, build consequential personal resources. To date, the empirical evidence for the build hypothesis has been largely indirect.
Prospective correlational studies have shown that people who, for
whatever reasons, experience or express positive emotions more
than others show increases over time in optimism and tranquility
(Fredrickson, Tugade, Waugh, & Larkin, 2003), ego-resilience
(Cohn, Fredrickson, Brown, Mikels, & Conway, 2008), mental
health (Stein, Folkman, Trabasso, & Richards, 1997), and the
quality of their close relationships (Gable, Gonzaga, & Strachman,
2006; Waugh & Fredrickson, 2006).
Here we present the first experimental evidence that directly tests
the build hypothesis. Such research has been virtually nonexistent (but
see Emmons & McCullough, 2003; King, 2001), largely because
resources are expected to accrue only after many experiences of
positive emotions over separate occasions, which necessitates a longitudinal design as well as a reliable, repeatable method for evoking
positive emotions. The well-documented hedonic treadmill effect
(Diener, Lucas, & Scollon, 2006) assures that emotion-elicitation
techniques used with success in the laboratory (e.g., film clips, gifts of
candy) would likely become inert if repeated daily. As the novelty of
an experience subsides, people’s emotions tend to revert to a trait-like
baseline. In this study, we sought to overcome this challenge by using
an induction based on meditation.
We suspected that meditation would outpace the hedonic treadmill for several reasons. First, it incorporates mindful attention,
which has been shown to undo hedonic adaptation (Schwarz,
Kahneman, & Xu, in press). Second, unlike watching a film or
receiving a gift, meditation practice is active and personalized.
Participants can lengthen the meditation, alter their focus, or
otherwise try to get more out of their practice, keeping it within a
range that is feasible but not boring. Most important, participants
can use the insights and psychological skills developed during
meditation practice in many situations and life domains. Meditation, then, offers opportunities for enhanced emotions throughout
the day, not simply during meditations, per se.
Meditation and mindfulness, which are perhaps best known as
elements of Buddhist spiritual practice, have also proven to be
fruitful topics within empirical research on well-being (Baer, 2003;
Kabat-Zinn, 2003; Segal, Williams, & Teasdale, 2002; Wallace &
Shapiro, 2006). For instance, for more than 2 decades, Kabat-Zinn
and colleagues have reported evidence that meditation helps people self-regulate stress, anxiety, chronic pain, and various illnesses
(for a review, see Kabat-Zinn, 2003). Building on the observation
that when formerly depressed individuals see their thoughts and
emotions from a wider perspective, they are more resistant to
relapse, Teasdale et al. (2000) developed a successful therapy that
combines mindfulness meditation with cognitive therapy.
More recently, Kabat-Zinn collaborated with Davidson et al.
(2003) to examine the affective, brain, and immunological effects
of beginning a meditation practice. Volunteers were randomly
assigned to either a waitlist control group (n ⫽ 16) or an 8-week
mindfulness-based stress-reduction workshop (n ⫽ 25), which
required a daily practice of guided meditation lasting about 1 hr.
As in past studies, trait anxiety was significantly reduced in the
meditation group. Both immediately after the training period and 4
months later, electroencephalogram monitoring revealed that meditators showed increases in left-sided anterior brain activation,
which has been repeatedly linked to greater positive, approachrelated emotions (for a review, see Davidson, 2000). Meditators
also showed a more robust and effective immune response to an
influenza vaccine administered at the end of the training period,
and the strength of this response was correlated with the magnitude
of left-sided anterior brain activation. The suggestion that meditation practice increases positive affect is also supported by at least
one experience sampling study (Easterlin & Cardeña, 1998).
Most empirical work on meditation has centered on mindfulness
meditation (e.g., Davidson et al., 2003; Teasdale et al., 2000). Because
we were particularly interested in evoking positive emotions, we
employed a related mind-training practice, loving-kindness meditation (LKM). LKM is a technique used to increase feelings of warmth
and caring for self and others (Salzberg, 1995). Like other meditation
practices, LKM involves quiet contemplation in a seated posture,
often with eyes closed and an initial focus on the breath. Yet whereas
mindfulness meditation involves training one’s attention toward the
present moment in an open-minded (nonjudgmental) way, LKM
involves directing one’s emotions toward warm and tender feelings in
an open-hearted way. Individuals are first asked to focus on their heart
region and contemplate a person for whom they already feel warm
and tender feelings (e.g., their child, a close loved one). They are then
asked to extend these warm feelings first to themselves and then to an
ever-widening circle of others. Thus, LKM may well cultivate broadened attention in addition to positive emotions. According to the
broaden-and-build theory, these two experiential consequences go
hand in hand.
In LKM, people cultivate the intention to experience positive
emotions during the meditation itself, as well as in their life more
generally. Moreover, mind-training practices like LKM are thought to
not only shift people’s fleeting emotional states but also reshape their
enduring personality traits (Davidson et al., 2003), a coupling of
momentary with long-term gains fully compatible with the broadenand-build theory. We acknowledge that mind-training practices, including LKM, are not simply vehicles for improving emotion experiences. The primary goal within contemplative traditions is, instead,
to learn about the nature of one’s mind and dispel false assumptions
about the sources of one’s happiness (Dalai Lama & Cutler, 1998).
These insights can, in turn, shift people’s basic outlooks on themselves in relation to others, increasing empathy and compassion.
Approaching daily life with the new insights and outlooks developed
through mind-training practice is what is thought to enhance people’s
POSITIVE EMOTIONS BUILD RESOURCES
emotion experiences. That said, the goal of the present study was to
test the build hypothesis, which required a means of reliably eliciting
positive emotions over the span of months. We saw LKM as a suitable
vehicle to meet this goal. Future empirical work is needed to test
whether the cognitive shifts outlined by scholars of contemplative
practices are indeed responsible for any success LKM has in enhancing positive emotions.
LKM involves a range of thoughts and visualizations, and it
directly evokes only select positive emotions (i.e., love, contentment, and compassion) and carries some potential to evoke negative emotions. Moreover, given the possibility of gradual shifts in
people’s outlooks and personality traits, we expected the positive
emotions generated by LKM to increase over time. Our study
involved daily assessments of time spent meditating and of a wide
range of discrete positive and negative emotions. This strategy
allowed us to determine whether (a) positive emotions, measured
directly, are responsible for any changes produced by LKM; (b)
different classes of positive emotions (low- vs. high-arousal, e.g.,
contentment vs. amusement; or self- vs. other-focused, e.g., pride
vs. love) are differentially induced by this practice; and (c) the
effects of LKM on positive emotions increase (because of practice)
or decrease (because of adaptation) over time.
We are aware of only one other field experiment that has tested
the effects of LKM. Carson et al. (2005) compared a group of
chronic pain patients who were taught LKM (n ⫽ 18) with a group
receiving standard care (n ⫽ 25). Results from this pilot trial
indicated that LKM reduced pain, anger, and psychological distress. The present study tests LKM in a larger sample, with a wider
variety of outcome measures. Most critically, it gathers detailed
data on positive emotions as a potential mediator of the benefits of
this form of meditation.
Overview of Empirical Strategy
We conducted a randomized, longitudinal field experiment to
test whether positive emotions, induced through LKM, build consequential personal resources. In designing our experiment, we
grappled with selecting the most appropriate comparison condition. In laboratory research, we have used sham meditation (i.e.,
sitting with eyes closed) to achieve precise experimental control.
For a 7-week intervention that asked participants for a substantial
investment of time and effort, both ethical and face-validity concerns led us away from this sort of placebo meditation. Another
approach is to choose a comparison condition that best addresses
the current state of knowledge in a given area. Our review of the
scientific literature had uncovered no published evidence that
LKM could produce sustained increases in positive emotions and
only limited and indirect evidence that positive emotions could
build personal resources. Given this embryonic state of evidence,
an appropriate initial comparison group would reflect treatment as
usual, which, outside the clinical literature, is perhaps better
phrased as life as usual. Thus, we chose a waitlist control design,
which can assess treatment efficacy while controlling for selfselection, history, maturation, regression to the mean, and the
effects of repeated testing (Chambless & Hollon, 1998; Kazdin,
2003). Although the groups differ in terms of experimenter demand, delivery format, and expectation of improvement, we address these limitations procedurally and analytically to the extent
possible (see Discussion).
1047
In the context of a workplace wellness program, we offered a
7-week meditation workshop to employees interested in stress
reduction and willing to respond to questionnaires and provide
daily, web-based reports of their emotions. All volunteers completed an initial survey that assessed their life satisfaction, depressive symptoms, and status on a range of personal resources.
Volunteers were then randomly assigned to either our meditation
workshop or a waitlist control group (which received the same
workshop after the study ended). Over the next 9 weeks (including
1 week before and after the workshop), participants in both groups
completed daily reports of their emotion experiences and meditation practice. About 2 weeks after the workshop ended, participants completed a final survey that reassessed their life satisfaction, depressive symptoms, and status on the same personal
resources measured previously.
In addition to daily reports of emotion experiences, which may
well underestimate the frequency of emotion experiences, at the
time of the final survey, participants also completed a detailed
account of the emotions they experienced that particular day using
the day reconstruction method (DRM; Kahneman, Krueger, Schkade, Schwarz, & Stone, 2004). The DRM is a survey method that
builds on the strengths of two older methods: time-use assessment
and momentary data capture (i.e., experience sampling). Like each
of these earlier methods, the DRM minimizes recall biases and
provides a comprehensive picture of daily experience. Participants
first reconstruct a detailed diary of “this morning” by dividing it
into sequences of episodes. Next, they complete a series of questions, including emotion reports, for each episode of their morning.
We predicted that participation in the 7-week LKM workshop
would increase individuals’ daily experiences of positive emotions,
over time across the 9 weeks of daily reporting and within the specific
morning targeted by the DRM. Drawing from the broaden-and-build
theory, we further predicted that increases in positive emotions, produced by LKM, would, in turn, build participants’ personal resources.
To test the generality of the build effect of positive emotions, we
targeted a wide range of personal resources, including cognitive
resources (e.g., mindfulness, the ability to savor positive experiences),
psychological resources (e.g., ego-resilience, environmental mastery),
social resources (e.g., positive relations with others, social support
given and received), and physical resources (e.g., illness symptoms,
duration of sleep). Finally, we investigated whether these resources
actually made a difference in participants’ lives. To do so, we tested
whether any increments in resources, in turn, contributed to changes
in overall life satisfaction, a judgment of fulfillment and well-being
that differs from positive affectivity in its global focus and cognitive
emphasis (Lucas, Diener, & Suh, 1996). As a secondary way to assess
whether newly built resources were consequential, we tested whether
they led to decreases in depressive symptoms. We distill this series of
predictions into the following overarching mediational hypothesis:
Hypothesis: Becoming skilled in LKM will, over time, increase people’s daily experiences of positive emotions,
which, in turn, build a variety of personal resources that hold
positive consequences for the person’s mental health and
overall life satisfaction.
Figure 1 portrays the conceptual model that underlies the build
hypothesis as we tested it here. Note that this study does not
directly assess momentary changes in broadened cognition, be-
1048
Figure 1. Conceptual model depicting predicted causal paths between loving-kindness meditation, change in
positive emotions, change in resources, and change in life satisfaction.
cause of the lack of valid measures that could be used repeatedly
and in the field, nor does it directly assess the cognitive shifts
produced by LKM that trigger positive emotions. As such, this
study evaluates positive emotions as a mechanism for the effects of
LKM but does not further decompose the mechanisms by which
LKM and positive emotions exert their influence.
Method
Participants
The study was conducted at the Compuware Corporation, a
large business software and information technology services company in Detroit, Michigan. All full-time employees working at
Compuware’s Detroit headquarters (approximately 1,800 individuals, 38% female, 34% ethnic minorities) received an e-mail
message from Compuware executives inviting them to participate
in the study.1 The study was described as a scientific investigation
of “the benefits of meditation. . . [to] reduce stress.” The e-mail
included a link to a website where employees could learn more
about the project. The information made clear that the study was
being conducted by university researchers, that the results would
be confidential, and that the choice of whether to participate would
not affect their standing with their employer.
Two hundred two Compuware employees attended the study
orientation, gave their consent, and completed the initial survey.
Of these, 102 were assigned to the LKM group and 100 were
assigned to the waitlist control group. Participants were excluded
from analyses for the following reasons: (a) They violated random
assignment (n ⫽ 7), (b) they failed to complete Time 2 measures
(n ⫽ 27), (c) they were assigned to the meditation condition but
attended fewer than three of the six weekly classes (n ⫽ 5), or (d)
they completed fewer than 30 of the 61 daily reports (n ⫽ 24). In
total, 63 participants were excluded, 34 from the LKM group and
29 from the waitlist group. Attrition and disqualification affected
the LKM and waitlist groups equally, ␹2(1, N ⫽ 202) ⫽ 0.4, p ⫽
.51, and was comparable with other studies on meditation (Carson
et al., 2005; Davidson et al., 2003; Teasdale et al., 2000). The final
sample, then, consisted of 139 participants, 67 of whom were in
the LKM group and 72 of whom were in the waitlist control group.
Demographic information is presented in Table 1. The compositions of the initial and completer samples were similar: Most
participants were female, most had bachelor’s or master’s degrees,
and the average age was 41 years (SD ⫽ 9.6). The completer
sample was 65.5% female, 73.7% White, 9.5% Black, 8.8% South
Asian, 6.6% East Asian, 0.7% Hawaiian/Pacific Islander, and
0.7% Hispanic. Male participants were disproportionately lost to
attrition and disqualification, ␹2(1, N ⫽ 180) ⫽ 10.9, p ⫽ .001.
There was also a trend towards loss of married participants, ␹2(1,
N ⫽ 178) ⫽ 3.2, p ⫽ .07. These groups, however, were lost
equally between conditions (waitlist ⫽ 64% female, meditators ⫽
67% female), ␹2(1, N ⫽ 139) ⫽ .17, p ⫽ .69, (waitlist ⫽ 56%
married, meditators ⫽ 60% married), ␹2(1, N ⫽ 137) ⫽ .22, p ⫽
.67, implying that married and male participant attrition related to
the study in general and not to LKM. Otherwise, the initial and
completer samples did not differ on demographic characteristics,
condition assignment, or depression and life-satisfaction scores
( p ⬎ .24). Four participants in the completer sample had a meditation
practice at the start of the study. Although these participants were
higher than others on positive emotions throughout the study, removing their data did not alter the pattern of findings reported here.
In addition to providing access to the participant pool, Compuware supported this study in multiple ways. All study orientation
meetings and meditation workshop sessions were held during
business hours at Compuware’s Detroit office. The meditation
workshops were offered free of charge to all interested employees.
Compuware also provided employee release time so that participants could attend a study orientation meeting, six meditation
workshop sessions, and complete all web surveys during work
time, without loss of compensation.
Participants received monetary compensation for time spent on
study measures. They received $10 for completing the initial
survey, $20 for completing the final survey, and $1 for each daily
report. In addition, participants who completed daily reports for at
least 40 of the 61 days received a $10 bonus and a copy of a
popular book on meditation by Jon Kabat-Zinn (valued at $24.95).
The total possible payment for the study was $101, plus the book.
Procedure
All study orientation sessions were held during employees’
lunch hour, in a large auditorium on Compuware premises. At
orientation, Barbara L. Fredrickson or Michael A. Cohn introduced
interested employees to the rationale for investigating the effects
of meditation on health and well-being. We sought to enhance
prospective participants’ investment in the study by describing
benefits of meditation already featured in the popular press and
regularly used to draw attendees to comparable workplace wellness courses, specifically, the potential to reduce stress and improve health and well-being. We also described the timeline of the
study and the details of compensation and explained the value of
gathering data from a waitlist control group. We did not describe
LKM, the broaden-and-build theory, our hypotheses regarding
mediation by daily positive emotions, or other information that
might have created detailed expectancy or demand effects. Those
who could not attend an orientation session received information
by phone.
Within the week following orientation, interested employees
logged on to a secure website, gave consent to participate in the
1
The population was limited to those Compuware employees with a
Compuware e-mail address. This included executives, developers, and
administrators, but not maintenance workers or cleaning staff.
1049
Table 1
Participant Demographics
Participant characteristic
Intent-to-treat
Per-protocol
Completersa
N
% providing demographic informationb
% in meditation group
% female
Agec
Education levelc
% married
Incomec ($)
Depressiond (CES–D, full scale)
Baseline
Posttest
Life satisfaction (SWLS)
Baseline
Posttest
% White (non-Hispanic)
195
88.2
49.2
59.8
41
Bachelor’s degree
60.5
⬎85,000
175
93.9
43.4
60.8
41
Bachelor’s degree
59.8
⬎85,000
139
100.0
48.2
65.5
41
Bachelor’s degree
57.7
⬎85,000
16.1
12.7
15.4
12.4
15.9
12.8
4.12
4.42
73.7
4.17
4.46
73.3
4.10
4.50
72.6
Note. CES–D ⫽ Center for Epidemiological Studies—Depression Measure (Radloff, 1977). SWLS ⫽ Satisfaction With Life Scale (Diener, Emmons, Larsen, & Griffin, 1985).
a
For exclusion criteria, see Methods section.
b
Twenty-three participants declined to provide demographic information. Median and percentage calculations
use only participants who provided data. Group-assignment data were available for all participants.
c
Value reported is median.
d
To facilitate comparison with previously published work, we report values that represent scores based on the
full CES–D scale, including both positively and negatively worded items. In subsequent analyses, we omit the
positively worded items to minimize conceptual overlap with positive emotions.
study, and responded to the initial (T1) survey (described below).
Participants learned their group assignment (meditation workshop
or waitlist control) only after completing the T1 survey.
The daily reporting phase of the study began 1 week following
orientation and continued for approximately 9 weeks. Each day,
participants visited our secure website to complete a short report
on their emotions and time spent in “meditation, prayer, or solo
spiritual activity” over the past day. After approximately 1 week of
baseline reporting, workshop classes and daily practice began for
the meditation group (described below). Daily reporting continued
for approximately 1 week after the meditation workshop ended.
After the daily reporting phase ended, the final (T2) survey
became available online. Participants visited our website a final
time and completed the same measures as at T1, followed by a day
reconstruction (described below) and a demographics questionnaire. After data collection was completed, participants received
debriefing information explaining more about the details of the
study.2 Approximately 2 months later, meditation classes began
for the waitlist control group. No further data were collected at that
time.
The websites for the initial questionnaires and the daily reports
were available around the clock. The final survey was available only
between noon and 2:00 a.m., because of the specifics of the DRM.
Although participants were encouraged to complete the surveys at
work, they were asked to practice meditation at home. Participants
who missed more than three consecutive weekday report forms, or
who did not fill out the final survey, received an automated e-mail
reminder asking them to visit our website. The study team did not
otherwise initiate contact with participants.
LKM Workshops
The meditation training involved six 60-minute group sessions
(held over 7 weeks, because of religious holidays) with 20 –30
participants per group. All sessions were led by a stressmanagement specialist (Sandra M. Finkel) with extensive experience practicing and teaching LKM. The median number of sessions attended was five (M ⫽ 4.3, SD ⫽ 1.8). At the first session,
participants were given a CD that included three guided meditations of increasing scope, led by the workshop instructor. During
Week 1, participants practiced a meditation directing love and
compassion toward themselves. During Week 2, the meditation
added loved ones. During subsequent weeks, the meditation built
from self, to loved ones, to acquaintances, to strangers, and finally,
to all living beings. The first meditation lasted 15 min, and the
final one lasted 22 min.
Each workshop session included 15–20 min for a group meditation, 20 min to check on participants’ progress and answer
questions, and 20 min for a didactic presentation about features of
the meditation and how to integrate concepts from the workshop
into one’s daily life. Participants were assigned to practice LKM at
home, at least 5 days per week, with the guided recordings. The
text of the guided meditations and week-by-week content outlines
are available by request from Sandra M. Finkel.3
Measures
Cognitive Resources: T1 and T2
Mindfulness and Awareness Scale. The Mindfulness and
Awareness Scale (Brown & Ryan, 2003) assesses awareness of
2
We contacted participants who did not complete the T2 survey to
request demographic information and to make the debriefing information
available.
3
Sandra M. Finkel can be reached by e-mail at smfinkel@med
.umich.edu
1050
one’s circumstances, as well as tendencies towards automated,
“mindless” behavior or acting on “autopilot.” Participants indicate
the frequency of 15 behaviors on a 6-point scale (1 ⫽ almost
always, 6 ⫽ almost never). Items include “I snack without being
aware of what I am eating” and “I could be experiencing some
emotion and not be conscious of it until some time later.” All items
are reverse-scored. (␣T1 ⫽ .86, ␣T2 ⫽ .89).
Agency thinking and pathways thinking. We used the Trait
Hope Scale (Snyder et al., 1991; Snyder, Rand, & Sigmon, 2002)
to assess these two cognitive components of Snyder’s hope theory.
Participants use a 4-point scale to indicate agreement or disagreement (1 ⫽ definitely false, 4 ⫽ definitely true) with 10 items
divided between two subscales: agency thinking (belief that one
has been/will be personally able to achieve one’s goals), including
“I meet the goals I set for myself” (␣T1 ⫽ .84, ␣T2 ⫽ .81), and
pathways thinking (belief that there are multiple ways to achieve
one’s goals), including “There are lots of ways around any problem” (␣T1 ⫽ .84, ␣T2 ⫽ .83).
Savoring Beliefs Inventory. The Savoring Beliefs Inventory
(Bryant, 2003) assesses one’s tendency to enjoy pleasant experiences in the moment (savoring the present), pleasurably anticipate
them beforehand (savoring the future), and pleasurably recall them
afterward (savoring the past). Participants indicate agreement on a
7-point scale with 24 items, including “It’s easy for me to rekindle
the joy from pleasant memories” and “When I think about a
pleasant event before it happens, I often start to feel uneasy or
uncomfortable” (reverse scored; savoring the past, ␣T1 ⫽ .88,
␣T2 ⫽ .92; savoring the present, ␣T1 ⫽ .88, ␣T2 ⫽ .89; savoring
the future, ␣T1 ⫽ .87, ␣T2 ⫽ .91).
Psychological Resources: T1 and T2
Life Orientation Test. The Life Orientation Test—Revised
(Scheier, Carver, & Bridges, 1994) is a 6-item scale that assesses
generalized optimism as the belief that positive things are possible
in the future. Participants indicate agreement or disagreement on a
5-point scale (1 ⫽ I agree a lot, 5 ⫽ I disagree a lot) with 10
statements (4 items are fillers), including “In uncertain times, I
usually expect the best” and “If something can go wrong for me,
it will” (reverse scored; ␣T1 ⫽ .82, ␣T2 ⫽ .79).
Ego-resilience. The ego-resilience measure (Block & Kremen,
1996) assesses the ability to bounce back from adversity and
flexibly adapt to shifting demands. Participants indicate agreement
or disagreement on a 4-point scale with 14 items, including “I
quickly get over and recover from being startled” and “I like to do
new and different things” (␣T1 ⫽ .73, ␣T2 ⫽ .74).
Psychological well-being. We measured five additional psychological resources using subscales of Ryff’s (1989) broader psychological well-being measure. Participants indicate agreement on a
6-point scale (1 ⫽ strongly disagree, 6 ⫽ strongly agree) with seven
to eight items for each of the following five subscales: personal
growth, with items like “For me, life has been a continuous process of
learning, changing, and growth” (␣T1 ⫽ .76, ␣T2 ⫽ .80); environmental mastery, with items like “I often feel overwhelmed by my
responsibilities” (reverse scored; ␣T1 ⫽ .78, ␣T2 ⫽ .80); autonomy,
with items like “I am not afraid to voice my opinions, even when they
are in opposition to the opinions of most people” (␣T1 ⫽ .72, ␣T2 ⫽
.77); self-acceptance, with items like “I like most parts of my personality” (␣T1 ⫽ .88, ␣T2 ⫽ .86); and purpose in life, with items like “My
daily activities often seem trivial and unimportant to me” (reverse
scored; ␣T1 ⫽ .80, ␣T2 ⫽ .80).
Social Resources: T1 and T2
Dyadic Adjustment Scale. The Dyadic Adjustment Scale
(Spanier, 1976) measures social support as the amount of emotional support the participant provides to and receives from close
others. Using a 5-point scale (0 ⫽ not at all, 4 ⫽ an extreme
amount), participants respond to questions, including “On the
whole, how much do your friends and relatives make you feel
loved and cared for?” and “If one of your close friends got sick or
were injured in a car accident, how much could they count on you
to take care of them?” Items are divided into subscales for social
support given (␣T1 ⫽ .81, ␣T2 ⫽ .81) and social support received
(␣T1 ⫽ .83, ␣T2 ⫽ .83).
Positive relations with others. Our third index of social resources was drawn from Ryff’s (1989) psychological well-being
scale (see above). The 7-item subscale includes items like, “I know
that I can trust my friends, and they know they can trust me” and
“I often feel lonely because I have few close friends with whom to
share my concerns” (reverse scored; ␣T1 ⫽ .81, ␣T2 ⫽ .81).
Physical Resources: T1 and T2
Illness symptoms. This self-report measure assesses 13 common symptoms of illness or poor health, including headaches,
chest pain, congestion, and weakness (Elliot & Sheldon, 1998).
Participants use a 7-point scale to rate the frequency of each
symptom over the past month (1 ⫽ not at all, 7 ⫽ very frequently;
␣T1 ⫽ .82, ␣T2 ⫽ .84).
Sleep duration. This single item, extracted from the Pittsburgh
Sleep Quality Index (Buysse, Reynolds, Monk, Berman, & Kupfer,
1989), asks participants to respond to the question “During the past
month, how many hours of actual sleep did you get at night?”
Outcome Measures: T1 and T2
Satisfaction with life scale. We assessed cognitive evaluations
of life satisfaction with this five-item scale (Diener, Emmons,
Larsen, & Griffin, 1985). It assesses participants’ global satisfaction with their lives and circumstances. Participants indicate agreement with each item on a 7-point scale, including “So far I have
gotten the important things I want in life” (␣T1 ⫽ .88, ␣T2 ⫽ .90).
Center for Epidemiological Studies—Depression Measure.
We assessed depressive symptoms with the Center for Epidemiological Studies—Depression Measure (Radloff, 1977). We excluded the four positively worded items to minimize conceptual
overlap with positive emotions (see Moskowitz, 2003; Ostir et al.,
2000). On a 5-point scale, participants indicated how often they
had felt symptoms of depression in the past week (0 ⫽ never, 4 ⫽
most of the time), including “I felt that I could not shake off the
blues even with help from my family or friends” (␣T1 ⫽ .86,
␣T2 ⫽ .88).
Emotions and Meditation Practice: Daily Assessments
During daily reports, participants completed the Modified
Differential Emotions Scale (mDES; Fredrickson et al., 2003).
The mDES asks participants to recall the past 24 hr and rate
their strongest experience of each of 19 specific emotions on a
4-point scale (0 ⫽ not at all, 4 ⫽ extremely). The emotions
listed were amusement, anger, awe, compassion, contempt,
contentment, disgust, embarrassment, gratitude, hope, joy, interest, love, pride, guilt, sadness, shame, fear, and surprise.
Participants also reported whether they had engaged in “meditation, prayer, or solo spiritual activity” since the last time they
filled out the survey (not necessarily the same 24-hr time span
as mDES responses). Both meditation and waitlist participants
responded to these questions.
DRM: T2
1051
2.
an intent-to-treat sample (n ⫽ 195), comprising all of the
participants who were successfully randomly assigned to
experimental condition; and
3.
a per-protocol sample (n ⫽ 175), comprising (a) all of the
participants successfully randomly assigned to the waitlist control condition (n ⫽ 98) and (b) those participants
assigned to LKM who received a predetermined “minimum effective dose” of LKM training (at least three of
the six weekly loving-kindness sessions; n ⫽ 77).
Analyses with the complete data sample are described below.
At the end of the section, analyses with the other samples are
discussed.
We used the DRM (Kahneman et al., 2004) to assess participants’ time-varying emotion experiences during a specific day.
Because of time constraints, we limited our assessment to the
morning of the targeted day. We asked participants to divide their
morning—from the time they awoke until they completed
lunch—into a continuous series of episodes and to provide a
descriptive label for each episode. We allowed a maximum of
10 episodes. Thereafter, participants revisited each labeled episode to provide ratings from 0 (not at all) to 4 (extremely) for
the emotion adjectives from the mDES, as described above
(Fredrickson et al., 2003). For each episode, participants were
also asked “What were you doing?” followed by a checklist of
several activities that included “praying/worshiping/meditating.” They also responded “yes” or “no” to the question, “Were
you interacting with anyone (including on the phone, in a
teleconference, etc)?”
Time spent in “meditation, prayer, or solo spiritual activity” was
assessed each day. As expected, during the baseline period, meditators and control participants did not differ in duration of meditative activity, t(135) ⫽ ⫺0.25, p ⫽ .80 (Ms ⫽ 13 and 12
min/week, respectively). Beginning with Week 1 of the study, and
for each subsequent week, participants in the LKM group engaged
in significantly more meditative activity than did those in the
control group, averaging about 80 min/week, although this
dropped to about 60 min/week after the workshop ended.
Results
Effects of LKM on Emotions
Overview of Data Analytic Strategy
Given the complexity of the data set, we performed a range of
analyses, which we forecast here.4 As a manipulation check, we
used t tests to confirm that participants in the LKM condition
were, in fact, meditating and were meditating more than the
control participants. A series of hierarchical linear models, with
time nested within individual—also known as growth models—
investigated the impact of experimental condition, passage of
time, and time spent meditating on self-reported emotions. An
additional set of analyses examined participants’ emotions
within a single morning, incorporating information about the
amount of time that participants had meditated over the course
of the study and whether they had meditated on the particular
morning in question.
We then tested the build hypothesis in a combined latent growth
curve and path-analysis structural equation model (SEM). The
growth curve for positive emotions from the hierarchical linear
model analyses was reparameterized as a SEM-based latent trajectory model. In the path-analysis portion of the model, baseline
positive emotions and slope of change in positive emotions predicted change in the targeted resource, which then predicted
change in life satisfaction or depression. Each of the 18 resources
we measured was tested in a separate model.5
Results were analyzed separately in three samples:
1.
individuals who adhered to the study requirements described above (our “complete data” sample, n ⫽ 139);
Manipulation Check
Did Participants in the Meditation Condition Comply
With Instructions to Meditate?
Did LKM Impact Positive Emotions Over the Course of
the Study?
We averaged measurements for nine positive emotions—
amusement, awe, contentment, joy, gratitude, hope, interest, love,
and pride—within each day, and then we averaged these daily
means over the week to create a composite positive emotions
variable for each week of the study. Across weeks, this index score
had an average alpha coefficient of .94 (range ⫽ .94 –.95).
The impact of LKM on positive emotions over time was tested
using hierarchical linear modeling, with time nested within individual. Experimental condition, week in the study, and their interaction were included as predictors. The model also included random effects for the intercept, which represented each participant’s
4
Preliminary analyses incorporated sex of participant as a predictor. It
did not significantly predict positive emotions, the impact of experimental
condition on positive emotions, or the impact of experimental condition on
positive emotions over time. In addition, it was not related to the constructs
we examine in subsequent models. For this reason, all reported analyses
collapse across male and female participants, and we do not consider the
impact of participant sex further.
5
We explored whether we might reduce the number of models tested by
considering the 18 different resources assessed as indicators of either one latent
“resources” factor, or four latent factors distinguished by type of resource (e.g.,
cognitive, psychological, social, and physical resources). However, confirmatory and exploratory factor analyses suggested that no such reduction was
warranted. The correlation matrix is available on request.
1052
level of positive emotions at baseline, and for the impact of week
in the study, which represented each participant’s change in positive emotions over time. Both random effects were significant
(intercept variance ⫽ 0.34, SE ⫽ 0.05, p ⬍ .0001; week variance ⫽ 0.002, SE ⫽ 0.0006, p ⫽ .0002), indicating that participants varied in their baseline levels of positive emotions and
showed differing rates of change over time. The fixed effects for
experimental condition and week were not significant, but their
interaction was (b ⫽ 0.041, SE ⫽ 0.011, p ⫽ .0004). Thus, neither
time nor condition alone predicted positive emotions, but over
time, a difference between conditions emerged (see Figure 2). We
probed the interaction by treating time as the focal predictor and
experimental condition as the moderating variable (Preacher, Curran, & Bauer, 2006). These analyses revealed that time did not
significantly predict positive emotions for control participants
(b ⫽ ⫺0.008, SE ⫽ 0.0079, p ⫽ .31) but did significantly predict
positive emotions for participants in the LKM condition (b ⫽ 0.03,
SE ⫽ 0.008, p ⫽ .0001). Thus, these results confirm that LKM
increased participants’ positive emotions over the course of the
study.
We then tested similar growth models for each of the nine
positive emotions included in the composite. In all cases, neither
main effect was significant, but their interaction was significant.
(The sole exception to this was that interest also showed both main
effects; see Table 2.) These results suggest that the findings for the
composite positive emotions variable were not determined by any
single positive emotion and that it is appropriate to consider the
positive emotions collectively.
We tested an additional growth model that examined compassion
over the duration of the study. Neither the main effects for experimental condition and week, nor their interaction (b ⫽ 0.021, SE ⫽
0.016, p ⫽ .21), was significant. Visual inspection revealed the same
pattern for compassion as for the positive emotions, but the increase
over time for meditators was not statistically significant.
amine any changes in the impact of meditation practice on positive
emotions over the course of the study, we entered meditation
practice for each week of the study as a separate variable. We
deliberately left experimental condition in the model to test the
unique contribution of time spent meditating each week, above and
beyond the impact of participation in the workshop or interaction
with the meditation instructor. Unexpectedly, time spent in “meditation, prayer, or solo spiritual activity” significantly predicted
positive emotions during the baseline week before the workshops
began ( p ⫽ .05), even when we excluded the participants who
reported a preexisting meditation practice. After the first week of
meditation instruction, time spent in meditative activity predicted
positive emotions for all time points except Week 4 ( p ⫽ .08),
even after we controlled for the other predictors in the model.
These results are presented in Table 3.6
To estimate the impact of LKM instruction and practice on
positive emotions, we tested a separate model with the meditators
alone. By excluding the control participants, who were not receiving LKM instruction, we avoided diluting the estimate for the
impact of “meditation, prayer, or solo spiritual activity” on positive emotions with non-LKM forms of spiritual practice. In this
model, 1 hr of meditation practice during Week 2 was associated
with a 0.06-unit increase in positive emotions (SE ⫽ 0.03, p ⫽ .06)
on the 5-point Likert scale described above. This value increased
steadily during Weeks 3–7 of the study. By Week 7, each hour of
meditation practice was associated with a 0.17-unit increase in
positive emotions (SE ⫽ 0.03, p ⬍ .0001). These data suggest that
the dose-response relationship between the practice of LKM and
the experience of positive emotions tripled over the course of the
study. Furthermore, even though meditation practice dropped after
the workshop ended in Week 7, 1 hr of meditation practice in
Week 8 still exerted approximately the same influence on positive
emotions as it had in Week 7 (b ⫽ 0.18, SE ⫽ 0.05, p ⫽ .0004).
What Role Did Individual Effort Play in the Impact of the
Intervention on Positive Emotions?
Did LKM Influence Negative Emotions Over the Course
of the Study?
The impact of LKM on positive emotions might be expected to
be a function not only of experimental condition but also of
individual effort put into daily practice. We tested a growth model
for positive emotions that included the number of hours of meditation practice each week as a fixed effect, time-varying predictor,
along with time and experimental condition. To allow us to ex-
We also examined the impact of LKM on negative emotions
over the course of the study. Negative emotions were indexed by
a composite of daily ratings for anger, shame, contempt, disgust,
embarrassment, guilt, sadness, and fear. Across weeks, this index
score had an average alpha coefficient of .85 (range ⫽ .81–.90). As
described above for positive emotions, the model included experimental condition, week in the study, Time ⫻ Condition interaction, and hours of meditation practice each week. None of the
predictors were significant. Thus, neither experimental condition,
week in the study, their interaction (b ⫽ ⫺0.011, SE ⫽ 0.011, p ⫽
.28), nor time spent meditating during any weeks of the study
(range p ⫽ .11–.74) significantly influenced the negative emotions
sampled in this study.
2.9
2.8
Control
2.7
Meditation
2.6
2.5
2.4
Ba
se
li
W ne
ee
k
W 1
ee
k
W 2
ee
k
W 3
ee
k
W 4
ee
k
W 5
ee
k
W 6
ee
k
W 7
ee
k
8
Positive Emotions
3
Time
Figure 2. Positive emotions by experimental condition.
6
To address concerns about multicollinearity between experimental
condition and time spent meditating, we tested a separate model in
which number of hours of meditation practice was group meancentered. The pattern of significant findings was identical, except that
the impact of time spent meditating became nonsignificant for Week 3,
as well as Week 4. Table 3 reports the uncentered meditation time
values, for ease of interpretation.
1053
Table 2
Impact of Loving-Kindness Meditation on Specific Positive Emotions
Experimental condition
Experimental condition ⫻ week
Week
Emotion
Estimate
SE
p
Estimate
SE
p
Estimate
SE
p
Amusement
Awe
Contentment
Gratitude
Hope
Interest
Joy
Love
Pride
⫺0.112
⫺0.163
0.036
⫺0.010
⫺0.139
⫺0.421
0.0005
0.060
⫺0.249
0.125
0.123
0.120
0.141
0.127
0.136
0.124
0.134
0.1369
.37
.19
.76
.94
.28
.002
.997
.66
.07
⫺0.012
⫺0.0003
⫺0.002
0.0006
⫺0.006
⫺0.022
⫺0.013
⫺0.009
⫺0.016
0.009
0.010
0.011
0.010
0.010
0.011
0.010
0.010
0.010
.20
.97
.83
.96
.55
.05
.21
.33
.15
0.040
0.046
0.043
0.035
0.044
0.060
0.037
0.036
0.048
0.014
0.014
0.016
0.014
0.015
0.016
0.014
0.014
0.014
.003
.001
.006
.01
.003
.0002
.01
.009
.0008
Did LKM Influence Emotions Within a
Targeted Morning?
The DRM provided data on participants’ emotional experiences
within the episodes of an ordinary morning. This offered a window
into the impact of our intervention on emotional experiences in
response to specific daily events, rather than emotions summarized
over an entire day. Five participants did not provide DRM data,
leaving 134 for analysis. There were 918 episodes recorded in
total, with each participant reporting a mean of about seven episodes (M ⫽ 6.85, SD ⫽ 2.38). As with the daily reports, composite
scores of positive and negative emotions were computed by taking
the mean of positive items and negative items, respectively. Consistent with the daily reports, participants reported higher positive
emotions (M ⫽ 1.16, SD ⫽ 0.15) than negative emotions (M ⫽
0.15, SD ⫽ 0.28). Positive and negative emotions were largely
uncorrelated (r ⫽ ⫺.06, p ⫽ .09).
Multilevel random-coefficient regression modeling has been recommended for analyzing DRM data (Stone et al., 2006). We estimated a series of models predicting positive or negative emotions for
a given episode from experimental condition, total number of hours
engaged in meditative activity over the course of the study, the time
of day of the episode, whether the episode included meditation,
whether the episode included social interaction, and the interaction
between social interaction and total hours of meditative activity. This
Table 3
Impact of Experimental Condition, Week, and Time Spent
Meditating on Positive Emotions
Predictor
Estimate
SE
p
Intercept
Experimental condition
Week
Experimental Condition ⫻ Week
Time Spent Meditating ⫺ Baseline
Time Spent Meditating ⫺ Week 1
2.717
⫺0.124
⫺0.010
0.026
0.167
0.006
0.083
0.068
0.045
0.093
0.107
0.144
0.130
0.075
0.110
0.008
0.013
0.086
0.039
0.032
0.031
0.026
0.029
0.028
0.029
0.048
⬍.0001
.26
.20
.04
.05
.88
.01
.03
.08
.002
.0001
⬍.0001
.007
interaction term was included to explore whether LKM—which focused on kindness and compassion toward others— had a specific
influence on the participant’s response to interactions with others. All
quantitative predictors were mean centered.
We established that the best fitting unconditional models for
positive and negative emotions had significant random intercepts
( ps ⬍ .0001) and autoregressive covariance structures ( ps ⬍
.0001), indicating that participants began the day with significant
variability in their levels of positive and negative emotions and
that temporally close measures of emotion were more highly
correlated than more distant measures. Time of day positively
predicted positive emotions (b ⫽ 0.065, SE ⫽ 0.009, p ⬍ .0001),
whereas no time trend emerged for negative emotions (b ⫽ 0.002,
SE ⫽ 0.003, p ⫽ .54). These findings are consistent with diurnal
rhythms of positive emotions, which have been found to peak at
noon (Stone et al., 2006). Experimental condition was not significant for either positive or negative emotions (b ⫽ 0.067, SE ⫽
0.118, ns, and b ⫽ ⫺0.082, SE ⫽ 0.048, ns, respectively).
We next tested the total number of hours spent in meditative
activity (over the previous 9 weeks) as a predictor of emotional
experiences during the episodes of the targeted morning.7 A positive
effect of time spent meditating on positive emotions emerged, above
and beyond the effect of time (b ⫽ 0.033, SE ⫽ 0.010, p ⫽ .0008).
This was not true for negative emotions (b ⫽ ⫺0.005, SE ⫽ 0.004,
p ⫽ .2064). Hence, time spent in meditative activity over the previous
9 weeks was associated with more frequent positive emotions and no
change in negative emotions across episodes within the targeted
morning. We do not consider negative emotions further.
A small number of participants (n ⫽ 9) indicated in their DRM
responses that they had engaged in meditative activity that morning. To assess whether the target day’s meditative activity alone
could account for the significant effects on positive emotions
reported above, we reran the models in two ways. First, we added
meditation at episode as a time-varying predictor to test the effects
of engaging in meditative activity on positive emotions experienced during that same episode. Second, in place of the episodelevel predictor, we added a dummy variable indicating whether or
7
It is not surprising that experimental condition and time spent meditating were highly correlated, r(139) ⫽ 0.71, p ⬍ .0001. Thus, we examined them separately as predictors of emotions within the morning targeted
by the DRM.
1054
not participants meditated that day to test the effects of engaging
in meditative activity on positive emotions experienced that day.
Meditating during an episode predicted higher positive emotions
during that episode (b ⫽ 0.39, SE ⫽ 0.17, p ⫽ .0207) but did not
change the effect of hours engaged in meditative activity over the
previous 9 weeks on positive emotions experienced that morning
(b ⫽ 0.033, SE ⫽ 0.010, p ⫽ .0008). Meditating any time that
morning also predicted positive emotions experienced that morning (b ⫽ 0.52, SE ⫽ 0.23, p ⫽ .0247) but also did not change the
effect of total hours meditated throughout the study (b ⫽ 0.029,
SE ⫽ 0.010, p ⫽ .0031). Thus, we can attribute much of the
increase in positive emotions on this particular day to the time
participants had spent meditating over the last several weeks.
Taken together, these DRM findings indicate that (a) meditation
produces positive emotions during meditation practice; (b) these
positive emotions persist after the meditation session has ended;
and (c) over time, repeated LKM practice produces a cumulative
increase in positive emotions that appears on subsequent days,
whether or not the individual meditates on that day.
Previous research has shown that, in general, people experience
more intense positive emotions when interacting with others than
when alone (McIntyre, Watson, Clark, & Cross, 1991). We explored whether time spent meditating over the previous 9 weeks
differentially influenced participants’ experiences of positive emotions, depending on whether they were interacting with others or
not. We tested a model with time of day, social interaction, time
spent meditating over the previous 9 weeks, and the interaction
of social interaction and time spent meditating as predictors. The
slope for social interaction was allowed to vary (Varslope ⫽ 0.052,
SE ⫽ 0.25, p ⫽ .0187), confirming that interacting with others
predicted positive emotions differentially across individuals. Beyond the effects of time and hours spent in meditation, episodelevel social interactions (b ⫽ 0.232, SE ⫽ 0.059, p ⬍ .0001) and
the interaction between time spent meditating and social interactions (b ⫽ 0.014, SE ⫽ 0.006, p ⫽ .0363) predicted positive
emotions in that morning. That is, more time spent meditating was
associated with higher positive emotions, and this effect was
stronger during social interactions.
Testing the Build Hypothesis
We tested the full build hypothesis by combining a growth
model for positive emotions with an SEM path analysis. This
combined model used the strengths of growth modeling, which
considers individual trajectories of change over time, and path
analyses, which can examine direct and indirect effects in mediational models. The growth model for positive emotions was reparameterized as a latent trajectory model in an SEM framework
(Curran & Hussong, 2003). Experimental condition and time spent
meditating during the week predicted positive emotions for each
week of the study. Time spent meditating was entered as a timevarying predictor. An intercept and slope for positive emotions
over the course of the study were created by allowing the indicators for positive emotions, representing positive emotions during
each week of the study, to cross-load on both intercept and slope
latent variables. The latent variable that reflected the intercept of
positive emotions, at baseline, was created by fixing factor loadings for the indicators to 1.0. The latent variable that reflected
change in positive emotions over the course of the study was then
created by specifying factor loadings that reflected week in the
study (0.0 ⫽ baseline, 1.0 ⫽ Week 1, 2.0 ⫽ Week 2, etc.).
In the path-analysis portion of the model, the intercept, representing each participant’s initial level of positive emotions during
the baseline week, and slope, representing each participant’s rate
of change in positive emotions over time, predicted change in his
or her resources between T1 and T2, which then predicted change
in life satisfaction between T1 and T2. In other words, each
participant’s baseline level of positive emotions and individual rate
of change in positive emotions over the course of the study,
calculated within the latent trajectory portion of the model, became
predictors in the path-analysis portion of the models we tested.
Given our experimental design, only change in positive emotions
(i.e., slope) was predicted to build participants’ resources. Thus,
we predicted that the path from slope of positive emotions to
resources would be significant, but the path from baseline positive
emotions to change in resources would not. The resource variable
was a difference score that represented change between T1 and T2
in the specific resource featured within each model. We tested the
model for each resource assessed, and this was the only variable
that changed across models. Last, the life-satisfaction variable was
also a difference score, representing change in life satisfaction
between T1 and T2. Thus, the model examined whether initial
positive emotions and changes in positive emotions over the
course of the study predicted changes in resources over the course
of the study, which, in turn, predicted changes in life satisfaction
over the course of the study. Participants with greater increases in
positive emotions were hypothesized to exhibit greater increases in
resources and, in turn, life satisfaction. A diagram of the model
tested is depicted in Figure 3.
The model was tested for each of the 18 resources identified in
Table 4, using LISREL 8.80 (Jöreskog & Sörbom, 1996). A
multitude of factors may be associated with individual trajectories
of change over time, therefore, it is rare for growth models or
combined growth and path-analysis models to fit well when assessed using standard SEM fit indices, such as root-mean-square
error of approximation (Widaman & Thompson, 2003). For this
reason, it is noteworthy that each of the models we tested produced
an estimated root-mean-square error of approximation of less than
0.08 (range ⫽ 0.068 – 0.076), indicating an acceptable fit to
the data. Given that all of the models tested were acceptable fits to
the data, and that overall model fit was influenced by the fit of the
latent trajectory portion of the model (which was the same for each
resource), we examined the significance of the individual path
coefficients in the models for each resource to test the build
hypothesis.
As predicted, the path from baseline positive emotions to change
in resources (Path A) was not significant for any of the resources,
indicating that change in resources over the course of the study
was not significantly affected by participants’ initial levels of
positive emotions (see Table 4). The paths from change in positive
emotions (i.e., slope) to change in resources (Path B) and from
change in resources to change in life satisfaction (Path C) are
central to the build hypothesis. These paths were significant for 9
of the 18 resources tested: mindfulness, pathways thinking, savoring the future, environmental mastery, self-acceptance, purpose in
life, social support received, positive relations with others, and
illness symptoms. In other words, increases in positive emotions
over the course of the study were associated with significant
1055
Figure 3. Combined latent trajectory and path-analysis model. Avg. daily pos. emo. ⫽ average daily positive
emotion; PE ⫽ positive emotion; SWLS ⫽ Satisfaction With Life Scale (Diener, Emmons, Larsen, & Griffin, 1985).
increases in these resources, which were, in turn, associated with
significant increases in life satisfaction. Table 4 presents the parameter estimates for all path coefficients tested. The first two
columns of Table 5 present the amount of variance explained in the
changes in resource and life satisfaction variables when the predicted build paths were significant.
Six of the nine remaining resources showed significant paths
influencing life satisfaction (Path C) but were not significantly
influenced by change in positive emotions (Path B). These resources were agency thinking, savoring the past, savoring the
present, optimism, personal growth, and autonomy. This suggests
that these six measures are indeed consequential resources, even
though increases in positive emotions did not significantly augment them.
Did Changes in Positive Emotions Directly Influence Life
Satisfaction, in Addition to Their Indirect Influence
Through Built Resources?
We examined the possibility that changes in positive emotions
could exert a direct effect on increases in life satisfaction (Path D),
in addition to the indirect effects via built resources (Paths B and
C). To examine this, we tested a series of models that included a
direct effect from change in positive emotions to change in life
satisfaction. The “Path D” column in Table 4 presents the results
for this path coefficient. (In Table 4, the columns for Paths A, B,
and C report values for these path coefficients when Path D is not
in the model.) The direct effect from change in positive emotions
to change in life satisfaction was not significant for any of the
models tested, nor did the model fit significantly improve when
this path was included. For the nine resources that were found in
previous analyses to exhibit the predicted pattern of significant
build paths, these path coefficients remained significant when the
direct effect of change in positive emotions on change in life
satisfaction was included in the model. These results indicate that
changes in positive emotions only produced changes in life satisfaction to the extent that they built personal resources. This further
underscores the conceptual distinction between transient experiences of positive emotions and global judgments of life quality
(Cohn et al., 2008; Diener et al., 2006).
Did Experimental Condition and Time Spent Meditating
Directly Impact Resources and Life Satisfaction, in
Addition to the Impact They Exerted Via Their Influence
on Changes in Positive Emotion?
We also examined the possibility that experimental condition
and amount of time spent meditating directly influenced changes
in resources and life satisfaction, in addition to their indirect
influence via positive emotions. We tested this possibility in a new
series of models. For purposes of clarity, these paths are not
represented in Figure 3, but they entail direct effects from experimental condition and from each week’s variable for time spent
meditating to both change in resource and change in life satisfaction. These effects were generally nonsignificant, with values that
varied depending upon the path and the resource being tested.
There was one exception: The direct effect from time spent meditating in Week 2 to change in life satisfaction was significant for
each of the 18 resources tested (e.g., mindfulness, b ⫽ 0.49, z ⫽
2.47). Excluding this direct effect, there were other, isolated significant effects, which represented a total of 4% of the 360 path
coefficients estimated, but there was no pattern to these effects,
and they did not exceed the percentage of path coefficients that
would be expected on the basis of chance alone. These results
suggest that experimental condition and time spent meditating
1056
Table 4
Resource Models Tested With Life Satisfaction as Outcome Variable
RMSEA (90% CI)
␹2(137, N ⫽ 139)
Mindfulnessa
Agency thinking
Pathways thinkinga
Savoring the past
Savoring the present
Savoring the futurea
0.068 (0.051–0.083)
0.074 (0.058–0.089)
0.071 (0.056–0.087)
0.070 (0.053–0.085)
0.071 (0.055–0.086)
0.072 (.056–0.087)
224.58 ( p ⫽ .00)
241.15 ( p ⫽ .00)
234.24 ( p ⫽ .00)
228.99 ( p ⫽ .00)
232.88 ( p ⫽ .00)
235.94 ( p ⫽ .00)
Optimism
Ego-resilience
Personal growth
Environmental masterya
Autonomy
Self-acceptancea
Purpose in lifea
0.075 (0.059–0.090)
0.075 (0.059–0.090)
0.073 (0.057–0.088)
0.070 (0.054–0.086)
0.075 (0.059–0.090)
0.072 (0.056–0.088)
0.076 (0.060–0.091)
Psychological resources
243.70 ( p ⫽ .00) ⫺0.06 (z ⫽ ⫺0.70)
0.04 (z ⫽ 0.38)
243.13 ( p ⫽ .00) ⫺0.07 (z ⫽ ⫺0.83)
0.25 (z ⫽ 2.53)
237.88 ( p ⫽ .00)
0.00 (z ⫽ ⫺0.05)
0.14 (z ⫽ 1.42)
231.02 ( p ⫽ .00)
0.06 (z ⫽ 0.66)
0.33 (z ⫽ 3.37)
243.87 ( p ⫽ .00) ⫺0.08 (z ⫽ ⫺0.94) ⫺0.01 (z ⫽ ⫺0.07)
236.21 ( p ⫽ .00) ⫺0.08 (z ⫽ ⫺0.92)
0.27 (z ⫽ 2.77)
245.55 ( p ⫽ .00)
0.11 (z ⫽ 1.30)
0.29 (z ⫽ 2.95)
Resource tested
Path A
Path B
Path C
Path D
0.20 (z ⫽ 2.04)
0.17 (z ⫽ 1.74)
0.22 (z ⫽ 2.25)
0.15 (z ⫽ 1.52)
0.18 (z ⫽ 1.87)
0.20 (z ⫽ 2.08)
0.25 (z ⫽ 3.04)
0.36 (z ⫽ 4.46)
0.24 (z ⫽ 2.94)
0.18 (z ⫽ 2.17)
0.30 (z ⫽ 3.72)
0.28 (z ⫽ 3.38)
0.12 (z ⫽ 1.24)
0.11 (z ⫽ 1.15)
0.12 (z ⫽ 1.19)
0.15 (z ⫽ 1.49)
0.12 (z ⫽ 1.19)
0.12 (z ⫽ 1.22)
0.26 (z ⫽ 3.10)
0.14 (z ⫽ 1.65)
0.30 (z ⫽ 3.75)
0.38 (z ⫽ 4.86)
0.18 (z ⫽ 2.15)
0.42 (z ⫽ 5.45)
0.40 (z ⫽ 5.09)
0.16 (z ⫽ 1.64)
0.14 (z ⫽ 1.42)
0.13 (z ⫽ 1.37)
0.06 (z ⫽ 0.64)
0.17 (z ⫽ 1.72)
0.05 (z ⫽ 0.58)
0.07 (z ⫽ 0.71)
0.15 (z ⫽ 1.77)
0.21 (z ⫽ 2.54)
0.36 (z ⫽ 4.54)
0.15 (z ⫽ 1.57)
0.13 (z ⫽ 1.28)
0.08 (z ⫽ 0.79)
Cognitive resources
⫺0.10 (z ⫽ ⫺1.17)
⫺0.03 (z ⫽ ⫺0.38)
⫺0.03 (z ⫽ ⫺0.32)
0.05 (z ⫽ 0.51)
⫺0.13 (z ⫽ ⫺1.45)
⫺0.06 (z ⫽ ⫺0.74)
Social resources
Social support given
0.071 (0.055–0.087) 233.63 ( p ⫽ .00)
0.16 (z ⫽ 1.82)
Social support receiveda
0.072 (0.056–0.087) 235.84 ( p ⫽ .00) ⫺0.09 (z ⫽ ⫺0.98)
Positive relations with othersa 0.071 (0.055–0.086) 232.06 ( p ⫽ .00) ⫺0.01 (z ⫽ ⫺0.10)
Illness symptomsa
Duration of sleep
0.15 (z ⫽ 1.49)
0.25 (z ⫽ 2.54)
0.29 (z ⫽ 2.97)
Physical resources
0.071 (0.055–0.086) 232.82 ( p ⫽ .00) ⫺0.09 (z ⫽ ⫺1.01) ⫺0.24 (z ⫽ ⫺2.47) ⫺0.20 (z ⫽ ⫺2.37) 0.13 (z ⫽ 1.27)
0.072 (0.057–0.088) 236.80 ( p ⫽ .00) ⫺0.11 (z ⫽ ⫺1.22) ⫺0.14 (z ⫽ ⫺1.35)
0.01 (z ⫽ 0.14)
0.18 (z ⫽ 1.78)
Note. RMSEA ⫽ root-mean-square error of approximation; CI ⫽ confidence interval. Parameter estimates are reported in standardized units. Path D was
tested in a separate set of models, for which the RMSEA values and parameter estimates for Paths A, B, and C were slightly different than those listed
above. For purposes of brevity, we have not presented these slightly different values when Path D was incorporated in the model.
a
Model was a good fit for the data, and the predicted build-hypothesis paths (Paths B and C) were significant.
exerted their influence on resources and life satisfaction because of
their impact on positive emotions.
Do Positive Emotions Influence Depressive Symptoms
Through the Same Mechanism (i.e., Built Resources) by
Which They Influence Life Satisfaction?
To explore whether positive emotions might beneficially influence measures of negative psychological adjustment through the
same mechanisms by which they influence life satisfaction, we tested
a series of models for which depressive symptoms was the ultimate
variable in the model, replacing life satisfaction. In these models,
change in positive emotions predicted change in the resource,
which, in turn, predicted change in depressive symptoms. Model
fit, as determined by root-mean-square error of approximation,
remained acceptable. In addition, the predicted build paths were
significant for the same nine resources for which these paths were
significant when life satisfaction was the ultimate variable in the
models. These findings suggest that increases in positive emotions
decrease depressive symptoms through the same mechanisms by
which they increase life satisfaction: built resources.
We also examined the possibility that positive emotion directly
influenced depressive symptoms, in addition to its indirect impact
via built resources. In the first set of models, we examined the
significance of the direct effect from change in positive emotions
to change in depressive symptoms (Path D). Unlike the results for
life satisfaction, this path was significant for all models tested. In
addition, the overall fit of the models significantly improved for all
18 resources when this path was included in the model ( p ⬍
.0025). Even so, the predicted build paths remained significant for
eight of the nine previously significant resources. This pattern of
results suggests that increases in positive emotions influenced the
decline in depressive symptoms via both built resources and a
direct impact on depressive symptoms. The one resource for which
this was not the case was social support received, for which the
mediated build paths were not significant when Path D was included. Table 6 presents the parameter estimates for these models.
The last two columns of Table 5 present the amount of variance
explained in the changes in resource and depressive symptoms
variables when the predicted build paths were significant. In a
second set of models, we examined the direct effects from experimental condition and time spent meditating each week to the
changes in resources and depressive symptoms, for each of the
resources tested. Although isolated paths were significant, these
represented only 2.8% of the paths tested, and there was no
discernible pattern to which paths were significant.
Intent-to-Treat Analyses
To test for possible effects of differential participant completion
on our results, we repeated the analyses above using our intentto-treat and per-protocol samples. The impact of experimental
condition over time on positive emotions was not significant in
either the intent-to-treat, t(1380) ⫽ 1.37, p ⫽ .17, or per-protocol
1057
Table 5
Variance Explained in Change in Resources and Change in Life Satisfaction and Depression for
Significant Resources
Life-satisfaction models
Resource
⌬ resource R2
⌬ life satisfaction R2
Depression models (negative
symptoms only)
⌬ resource R2
⌬ depression R2
Mindfulness
Pathways thinking
Savoring the future
0.06
0.05
0.05
Cognitive resources
0.06
0.06
0.08
0.06
0.05
0.05
0.22
0.15
0.16
Environmental mastery
Self-acceptance
Purpose in life
0.10
0.09
0.08
0.15
0.18
0.16
0.10
0.09
0.08
0.25
0.24
0.29
Social support received
Positive relations with others
0.08
0.08
Social resources
0.04
0.13
0.08
0.09
0.15
0.24
Illness symptoms
0.06
Physical resources
0.04
0.05
0.18
Note. The variance estimates reported for the life-satisfaction models is for models that include Paths A, B, and
C, but not D, because this path was not significant for any of the life-satisfaction models tested. The variance
reported for the depression models is for models that include Paths A, B, C, and D, because path D was
significant for each of the depression models tested. Direct effects from experimental condition and time spent
meditating to resource and life satisfaction/depression were not included in any of the models.
samples, t(1380) ⫽ 1.58, p ⫽ .11, whereas it was significant in our
completer sample (discussed above). The impact of time spent
meditating on positive emotions remained significant in both samples, starting with the first week of instruction. The resources for
which we found significant build paths (paths from positive emotion change to resource to life satisfaction; shown as Paths B and
C in Figure 3) generally showed the same significant paths in the
intent-to-treat and per-protocol samples. Positive emotions significantly predicted savoring the future only in the completer sample,
and change in ego resilience significantly predicted change in life
satisfaction and depression in the intent-to-treat and per-protocol
samples, even though it did not do so in the completer sample.
Overall, the hypothesis that positive emotions help people build
consequential personal resources was supported in the intent-totreat analysis. However, these analyses suggest that conclusions
about the efficacy of LKM may need to be restricted to individuals
who invest adequate effort in training and practice (approximately
70% in this sample).
Discussion
The broaden-and-build theory (Fredrickson, 1998, 2001) states
that, over time, recurrent experiences of positive emotions allow
people to build consequential personal resources. The data reported
here provide the first experimental test of the build hypothesis. The
findings are clear cut: The practice of LKM led to shifts in people’s
daily experiences of a wide range of positive emotions, including
love, joy, gratitude, contentment, hope, pride, interest, amusement,
and awe. These increases in positive emotions were evident both
within the trajectories of change in daily emotions over the span of 9
weeks and within a detailed analysis of a given morning 2 weeks after
formal training ended. These shifts in positive emotions took time to
appear and were not large in magnitude, but over the course of 9
weeks, they were linked to increases in a variety of personal resources, including mindful attention, self-acceptance, positive relations with others, and good physical health. Moreover, these gains in
personal resources were consequential: They enabled people to become more satisfied with their lives and to experience fewer symptoms of depression. Simply put, by elevating daily experiences of
positive emotions, the practice of LKM led to long-term gains that
made genuine differences in people’s lives.
The conceptual model— drawn from the broaden-and-build theory
and depicted in Figure 1—is unambiguously supported by the evidence reported here. Most important, positive emotions emerge as the
clear centerpiece of the model. LKM was beneficial precisely because
it helped people experience positive emotions; direct effects of LKM,
circumventing the hypothesized build paths, were virtually nonexistent. Positive emotions emerged as the mechanism through which
people build the resources that make their lives more fulfilling and
help keep their depressive symptoms at bay.
These data also echo a message from our recent work that
unpacks the relationship between positive emotions and life satisfaction (Cohn et al., 2008). Although both can be considered facets
of happiness or subjective well-being (Lucas et al., 1996), we
found positive emotions, and not life satisfaction, to predict change
in resources. Furthermore, the association between increased positive emotions and increased life satisfaction was fully mediated by
resource building. This suggests that people judge their lives to be
more satisfying and fulfilling, not because they feel more positive
emotions per se, but because their greater positive emotions help
them build resources for living successfully.
1058
Table 6
Resource Models Tested With Depressive Symptoms (Negative Symptoms Only) as Outcome Variable
Resource tested
RMSEA (90% CI)
␹2(136, N ⫽ 139)
Mindfulnessa
Agency thinking
Pathways thinkinga
Savoring the past
Savoring the present
Savoring the futurea
0.070 (0.053–0.085)
0.074 (0.059–0.090)
0.072 (0.056–0.087)
0.069 (0.053–0.085)
0.073 (0.057–0.088)
0.072 (0.056–0.087)
227.53 ( p ⫽ .00)
240.88 ( p ⫽ .00)
233.37 ( p ⫽ .00)
226.33 ( p ⫽ .00)
237.09 ( p ⫽ .00)
233.93 ( p ⫽ .00)
Optimism
Ego-resilience
Personal growth
Environmental masterya
Autonomy
Self-acceptancea
Purpose in lifea
Social support given
Social support received
Positive relations with
othersa
Illness symptomsa
Duration of sleep
Path A
Path B
Path C
Path D
0.20 (z ⫽ 2.06)
0.18 (z ⫽ 1.77)
0.22 (z ⫽ 2.23)
0.16 (z ⫽ 1.59)
0.19 (z ⫽ 1.89)
0.21 (z ⫽ 2.15)
⫺0.28 (z ⫽ ⫺3.88)
⫺0.27 (z ⫽ ⫺3.41)
⫺0.17 (z ⫽ ⫺2.02)
⫺0.23 (z ⫽ ⫺2.81)
⫺0.42 (z ⫽ ⫺5.62)
⫺0.19 (z ⫽ ⫺2.37)
⫺0.31 (z ⫽ ⫺3.06)
⫺0.30 (z ⫽ ⫺3.25)
⫺0.31 (z ⫽ ⫺3.22)
⫺0.32 (z ⫽ ⫺3.39)
⫺0.27 (z ⫽ ⫺3.04)
⫺0.31 (z ⫽ ⫺3.24)
0.075 (0.059–0.090)
0.075 (0.059–0.090)
0.075 (0.059–0.090)
0.071 (0.056–0.087)
0.072 (0.057–0.087)
0.073 (0.058–0.089)
0.076 (0.061–0.091)
241.11 ( p ⫽ .00) ⫺0.06 (z ⫽ ⫺0.70)
0.04 (z ⫽ 0.40)
242.45 ( p ⫽ .00) ⫺0.07 (z ⫽ ⫺0.83)
0.25 (z ⫽ 2.53)
241.56 ( p ⫽ .00)
0.00 (z ⫽ ⫺0.04)
0.14 (z ⫽ 1.45)
232.54 ( p ⫽ .00)
0.06 (z ⫽ 0.69)
0.33 (z ⫽ 3.39)
243.07 ( p ⫽ .00) ⫺0.08 (z ⫽ ⫺0.94) ⫺0.01 (z ⫽ 0.07)
237.81 ( p ⫽ .00) ⫺0.08 (z ⫽ ⫺0.93)
0.27 (z ⫽ 2.78)
246.05 ( p ⫽ .00)
0.12 (z ⫽ 1.33)
0.29 (z ⫽ 2.97)
⫺0.10 (z ⫽ ⫺1.27)
⫺0.02 (z ⫽ ⫺0.25)
⫺0.35 (z ⫽ ⫺4.58)
⫺0.37 (z ⫽ ⫺4.61)
⫺0.08 (z ⫽ ⫺0.98)
⫺0.35 (z ⫽ ⫺4.36)
⫺0.43 (z ⫽ ⫺5.65)
⫺0.35 (z ⫽ ⫺3.63)
⫺0.35 (z ⫽ ⫺3.45)
⫺0.30 (z ⫽ ⫺3.35)
⫺0.24 (z ⫽ ⫺2.57)
⫺0.35 (z ⫽ ⫺3.66)
⫺0.25 (z ⫽ ⫺2.71)
⫺0.24 (z ⫽ ⫺2.66)
0.071 (0.055–0.087)
0.072 (0.056–0.088)
231.87 ( p ⫽ .00)
234.70 ( p ⫽ .00)
Social resources
0.16 (z ⫽ 1.81)
⫺0.08 (z ⫽ ⫺0.98)
0.16 (z ⫽ 1.59)
0.25 (z ⫽ 2.60)
⫺0.06 (z ⫽ ⫺0.69) ⫺0.35 (z ⫽ ⫺3.58)
⫺0.15 (z ⫽ ⫺1.80) ⫺0.32 (z ⫽ ⫺3.21)
0.071 (0.055–0.086)
230.43 ( p ⫽ .00)
⫺0.01 (z ⫽ ⫺0.08)
0.30 (z ⫽ 3.02)
⫺0.35 (z ⫽ ⫺4.33) ⫺0.25 (z ⫽ ⫺2.71)
0.072 (0.056–0.088)
0.073 (0.057–0.088)
234.74 ( p ⫽ .00)
235.69 ( p ⫽ .00)
Cognitive resources
⫺0.10 (z ⫽ ⫺1.18)
⫺0.03 (z ⫽ ⫺0.39)
⫺0.03 (z ⫽ ⫺0.31)
0.05 (z ⫽ 0.52)
⫺0.13 (z ⫽ ⫺1.46)
⫺0.07 (z ⫽ ⫺0.75)
Physical resources
⫺0.09 (z ⫽ ⫺1.02) ⫺0.24 (z ⫽ ⫺2.40)
0.26 (z ⫽ 3.17)
⫺0.29 (z ⫽ ⫺3.09)
⫺0.10 (z ⫽ ⫺1.11) ⫺0.15 (z ⫽ ⫺1.48) ⫺0.13 (z ⫽ ⫺1.50) ⫺0.37 (z ⫽ ⫺3.87)
Note. RMSEA ⫽ root-mean-square error of approximation; CI ⫽ confidence interval. Parameter estimates are reported in standardized units.
Model was a good fit for the data, and all predicted build-hypothesis paths (Paths B and C) were significant.
a
Nine of the eighteen resources we tested fit the hypothesized
build paths. Of the remaining nine, six showed changes in the
expected direction on the build paths (see Table 4). We speculate
that these six resources may be affected by positive emotions,
albeit less strongly or more slowly than other resources, and not
that the build hypothesis is categorically inapplicable to them.
The resources that did show significant build effects might be
loosely grouped into two categories. The first involves having a
loving attitude toward oneself and others and includes selfacceptance, social support received, and positive relations with others.
The second involves a feeling of competence about one’s life and
includes pathways thinking, environmental mastery, purpose in life,
and ego-resilience (which was influenced by positive emotions, although just shy of significantly influencing life satisfaction). We
speculate that increases in positive emotions may impact these resources more rapidly and to a greater extent than others.
This study confirms yet again that positive emotions are more
than momentary good feelings. Laboratory experiments have documented that positive emotions broaden cognition (for a review,
see Fredrickson & Cohn, 2008). Now we have evidence from a
field experiment to document that positive emotions also place
people on trajectories of growth, leaving them better able to ward
off depressive symptoms and become ever more satisfied with life.
This experiment also carries the inspiring implication that people
can take deliberate action to cultivate meaningful experiences of
positive emotion and reap these benefits as a result.
This field experiment also further documents the benefits of
meditation. When people initiated a practice of LKM, they enjoyed
payoffs both immediately, in terms of self-generated positive emotions, and over time, in terms of increased resources and overall
well-being. Meditators even experienced enhanced positive emotions in ordinary life situations, especially those involving other
people. This substantiates the claim that this type of meditation
changes the way people approach life.
We found that the effects of LKM were specific to positive
emotions, without a comparable decrease in negative emotions.
This resembles the work of Teasdale et al. (2000), who anecdotally
reported that their mindfulness-based protocol does not reduce
negative emotions but, instead, alters responses to negative emotions that can lead to depression (Segal et al., 2002). In contrast,
Carson et al. (2005) uncovered a marginal decrease in trait anger
in their pilot study of LKM. They also observed reductions in
anxiety and distress, but these may have been due to the study’s
central outcome of pain amelioration. Davidson et al. (2003) also
found a decrease in trait anxiety with mindfulness-based stress
reduction but only weak support for changes in negative emotion.
Future work will need to resolve these inconsistencies.
Another curious finding was the null effect of LKM on selfratings of compassion. In hindsight, we speculate that our sole
daily item for compassion (“In the past 24 hours, what is the most
sympathy, concern or compassion you have felt?”) may have
oriented respondents toward compassion felt in response to the
suffering of others, rather than kindness or equanimity per se. If
the suffering of others was not directly salient to participants on a
daily basis, increases on this particular item may have been lim-
ited. We suggest that future work tailor measures of compassion to
more directly reflect the teachings of contemplative traditions.
A final puzzling finding was the initially lower level of positive
emotions in the meditation group. We speculate that this difference
reflects the difficulties of initiating any self-change effort, even if
those changes are self-chosen. Consider the parallel to the perennial
New Year’s resolution to lose weight to be healthier. At the peak of
a person’s motivation to shed pounds, he or she might join a local
gym. Then, days later, the person realizes that he or she must actually
go to the gym and exercise. Starting a meditation practice may
similarly involve a period of doing something unfamiliar, difficult,
and draining without immediate rewards. Contemplative traditions
have articulated five obstacles facing novice meditators, including
craving, anger, boredom, restlessness, and doubt (Kabat-Zinn, 2005).
These obstacles are thought to result from increased awareness of
challenging inner states that may be commonly present, although not
noticed during one’s typical busy and outward-directed focus. Indeed,
nearly all attrition occurred during the initial weeks, when participants
may not have been sufficiently “in shape” to feel competent at
meditation or derive benefits from it. Yet if people can endure these
first difficult weeks, meditation becomes more effective, and positive
emotions begin to accumulate and compound, changing people for the
better.
Because we set out to develop a durable method of inducing
positive emotions, the dose-response results we documented are particularly inspiring. We found that the amount of positive emotions
participants gained per hour spent meditating increased over the
course of the study, tripling from the first week to the last. Rather than
becoming bored with or jaded to the effects of meditation, our
participants seemed to be building a dependable skill for selfgenerating positive emotions again and again. These findings are
especially noteworthy given that most of our participants were novice
meditators and our meditation workshop lasted only 7 weeks.
Limitations and Future Directions
This study breaks new ground in several ways, which leaves ample
room for future research to probe or refine its findings. First, the
sample was predominantly White, educated, and motivated for selfchange. Mindfulness-based programs have shown widespread emotional and medical benefits in diverse populations and for individuals
without prior interest in meditation (Kabat-Zinn, 1990), and it will be
important to determine whether the same holds true for LKM. Second, the duration of the experiment was just over 10 weeks. In the
future, it will be important to investigate the extent to which the
resources endure beyond the end of the intervention or into periods of
heightened stress or negative emotions. We found that after the formal
workshop ended, time spent meditating and positive emotions decreased in tandem, even though meditation remained effective at
evoking positive emotions. Lyubomirsky, Sheldon, and Schkade
(2005) have argued that intentional activity is required to sustain gains
in happiness. Future research will benefit from assessing the duration
of gains or determinants of continued, independent practice. Finally,
the current experiment did not include daily measures of broadened
cognition, which would have allowed a more precise test of the
proposed links between positive emotions, broadened thinking, and
resources. Currently, no measures of broadening are valid, repeatable,
and administrable outside the lab, but once one has been developed
1059
and validated, it will be an important contribution to this research
program.
Another necessity in future work will be to move beyond selfreport data to eliminate concerns of shared method variance. Implicit
or behavioral measures, observer reports, and physiological markers
will be especially useful. Specifically, researchers can track changes
in emotions over time with implicit or behavioral measures of affect
(Payne, Cheng, Govorun, & Stewart, 2005) or positivity bias
(Carstensen & Mikels, 2005). In a more recent study of LKM, we
obtained observer reports from peers identified by study participants.
Preliminary analyses suggest that, as expected, observers judge meditators to be more helpful than control participants (Fredrickson,
2008). Romantic partners, supervisors and physicians would also be
fruitful informants in future research. Finally, in current and planned
work, we are investigating whether LKM produces changes in respiratory sinus arrhythmia, progesterone, and oxytocin, each of which
has been linked to positive social relations (e.g., Brown et al., 2008;
Eisenberg et al., 1995; Holt-Lunstad, Birmingham, & Light, in press).
The comparison condition within this experiment was a waitlist
control group. Although typical for initial tests of psychological
interventions (e.g., Davidson et al., 2003; Teasdale et al., 2000),
this experimental design can inadvertently create experimenter
demand, expectation of improvement, or nonspecific effects related to delivery format. We address each possibility in turn: First,
the explicit focus on love and kindness may have created demand
to elevate self-reports of these emotions. However, our data indicated that (a) LKM was associated with changes in many positive
emotions, not just the ones explicitly discussed; (b) guided meditations featured the terms “love” and “compassion” beginning in
Week 1, yet the profile of changes in self-reported positive emotions (see Figure 2) shows that positive emotions did not significantly increase until Week 3; and (c) self-reported positive emotions fit into a full set of mediational pathways (see Figure 3),
which participants were unlikely to intuit and use to shape their
responses.8 Second, simply participating in a meditation workshop
might create the expectation of improvement. These expectations
might give rise to positive emotions, such as hope and confidence,
a legitimate, though nonspecific, effect of the intervention. We
underscore that the increase in positive emotions evident in the
current study did not appear until Week 3 (see Figure 2), whereas
placebo responses typically emerge rapidly (Scott et al., 2007).
Third, nonspecific effects of delivery format, including contact
with a caring instructor, group interaction, and weekly workrelease time might also have contributed to increases in positive
emotions. However, we found that when controlling for group
assignment, time spent meditating still predicted increases in positive emotions. Even among participants who received the nonspe8
Another way experimenter demand might have produced the results is if
meditation participants gradually began to skip responding on days low in
positive emotions. Meditation participants did respond less frequently over
time (dropping from 5.2 to 4.6 responses per week), whereas waitlist participants did not, F(7, 132) ⫽ 3.75, p ⫽ .002. However, the week-by-week
correlations between positive emotions and response frequency were very low
in both groups. The sole significant correlation suggested that, if anything, the
highest positive emotions were reported by participants who responded most
frequently (i.e., least selectively). Also, recall that emotion measures were
analyzed using per-participant means for each week, meaning that frequent
responders did not contribute disproportionately to the data.
1060
cific benefits, meditation itself—the proposed core of the intervention—predicted positive change. We also examined whether
participants reported a boost in positive emotions on the day of
workshop sessions or the day after, comparing waitlist participants, LKM participants who did not attend that week’s workshop,
and LKM participants who did. The results did not differ from
chance, suggesting that the higher positive emotions reported by
LKM participants reflected a continuous upward trend, rather than
a temporary response to the one day each week that involved time
off of work, social support, and contact with the instructor. Overall, patterns in our data argue against spurious results arising from
our use of a waitlist control group. Now that LKM has shown
efficacy in increasing positive emotions and building personal
resources, future work will be able to directly control for nonspecific effects and expectancies by comparing LKM with other
meditative or self-change techniques.
Another alternative explanation for our findings is that whatever
positive emotions our participants were feeling at T2 cast a rosy
glow over all their self-reports and artificially produced the appearance of growth in resources. The reports from the DRM
provided an estimate of positive emotions for the day the T2
measures were completed. We regressed that day’s positive emotions on aggregate positive emotions over the 9 weeks of daily
reports (R ⫽ .69) and created a residual term, representing positive
emotions that were present at T2 and could have cast a rosy glow
over responses but that were not present during the time resources
were being built. We tested the residual variable in our mediational
models, in place of change in positive emotions over time. It did
not predict change in any of the resources. This suggests that
positive emotions experienced over time exerted a gradual, cumulative effect, rather than simply biasing responding at the moment
participants were responding to T2 questionnaires.
Conclusion
One of the most deflating concepts facing positive psychology is
the hedonic treadmill (Brickman, Coates, & Janoff-Bulman, 1978):
Even though positive and negative events (e.g., winning the lottery, becoming paraplegic) temporarily alter levels of happiness,
people quickly adapt to them and return to a fixed emotional
set-point. The hedonic treadmill, as classically stated, implies that
all efforts to improve happiness are doomed to failure. Yet more
nuanced research (Diener et al., 2006) indicates that adaptation is
not necessarily inevitable and may be strongest for negative affect
and weaker for positive affect and life satisfaction. The evidence
reported here reveals that one way to outpace the hedonic treadmill
is to begin a practice of LKM. Participants who invested an hour
or so each week practicing this form of meditation enhanced a
wide range of positive emotions in a wide range of situations,
especially when interacting with others. We find these data especially promising. LKM appears to be one positive emotion induction that keeps on giving, long after the identifiable “event” of
meditation practice.
Positive emotions feel good, and feelings like love, joy, and
contentment can be valuable in and of themselves. Yet the
broaden-and-build theory posits that natural selection sculpted our
ancestors’ positive emotions to be useful in more far-reaching
ways as well. These desirable states built resources that gave our
ancestors’ an edge in circumstances that impinged on their sur-
vival. To our knowledge, this is the first experiment to provide
clear support for the build hypothesis. By random assignment, one
group of individuals began a mind-training practice that increased
their positive emotions and, in turn, their personal resources and
well-being. Just as the broaden-and-build theory predicts, then,
when people open their hearts to positive emotions, they seed their
own growth in ways that transform them for the better.
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Received April 27, 2007
Revision received April 26, 2008
Accepted June 3, 2008 䡲
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Am Psychol. 2001 March ; 56(3): 218–226.
The Role of Positive Emotions in Positive Psychology:
The Broaden-and-Build Theory of Positive Emotions
Abstract
In this article, the author describes a new theoretical perspective on positive emotions and situates
this new perspective within the emerging field of positive psychology. The broaden-and-build
theory posits that experiences of positive emotions broaden people's momentary thought-action
repertoires, which in turn serves to build their enduring personal resources, ranging from physical
and intellectual resources to social and psychological resources. Preliminary empirical evidence
supporting the broaden-and-build theory is reviewed, and open empirical questions that remain to
be tested are identified. The theory and findings suggest that the capacity to experience positive
emotions may be a fundamental human strength central to the study of human flourishing.
The mission of positive psychology is to understand and foster the factors that allow
individuals, communities, and societies to flourish (Seligman & Csikszentmihalyi, 2000).
What role do positive emotions play in this mission? On first consideration, the answer
seems simple: Positive emotions serve as markers of flourishing, or optimal well-being.
Certainly moments in people's lives characterized by experiences of positive emotions—
such as joy, interest, contentment, love, and the like—are moments in which they are not
plagued by negative emotions—such as anxiety, sadness, anger, and despair. Consistent with
this intuition, the overall balance of people's positive and negative emotions has been shown
to predict their judgments of subjective well-being (Diener, Sandvik, & Pavot, 1991).
Building on this finding, Kahneman (1999) suggested that “objective happiness” can best be
measured by tracking (and later aggregating) people's momentary experiences of good and
bad feelings (but see Fredrickson, 2000c). According to these perspectives, positive
emotions signal flourishing. But this is not the whole story: Positive emotions also produce
flourishing. Moreover, they do so not simply within the present, pleasant moment but over
the long term as well. The take-home message is that positive emotions are worth
cultivating, not just as end states in themselves but also as a means to achieving
psychological growth and improved well-being over time.
A review of current perspectives on emotions, affect, and their respective functions provides
an important backdrop. A selective review follows.
Perspectives on Emotions and Affect
Working definitions of emotions and affect vary somewhat across researchers. Yet despite
ongoing debate (e.g., Diener, 1999; Ekman & Davidson, 1994), consensus is emerging that
emotions are but a subset of the broader class of affective phenomena. Emotions, according
Copyright 2001 by the American Psychological Association, Inc.
Correspondence concerning this article should be addressed to Barbara L. Fredrickson, Department of Psychology, University of
Michigan, 525 East University Avenue, Ann Arbor, MI 48109-1109. [email protected]..
Author's note. Barbara L. Fredrickson, Department of Psychology and Research Center for Group Dynamics at the Institute for Social
Research, University of Michigan.
Fredrickson
Page 2
to this perspective, are best conceptualized as multicomponent response tendencies that
unfold over relatively short time spans. Typically, an emotion begins with an individual's
assessment of the personal meaning of some antecedent event. This appraisal process may
be either conscious or unconscious, and it triggers a cascade of response tendencies manifest
across loosely coupled component systems, such as subjective experience, facial expression,
cognitive processing, and physiological changes.
Affect, a more general concept, refers to consciously accessible feelings. Although affect is
present within emotions (as the component of subjective experience), it is also present
within many other affective phenomena, including physical sensations, attitudes, moods, and
even affective traits. Thus, emotions are distinct from affect in multiple ways. First,
emotions are typically about some personally meaningful circumstance (i.e., they have an
object), whereas affect is often free-floating or objectless (Oatley & Jenkins, 1996; Russell
& Feldman Barrett, 1999; Ryff & Singer, in press). Additionally, emotions are typically
brief and implicate the multiple-component systems described above, whereas affect is often
more long-lasting and may be salient only at the level of subjective experience (Ekman,
1994; Rosenberg, 1998; Russell & Feldman Barrett, 1999). Finally, emotions are often
conceptualized as fitting into discrete categories of emotion families, like fear, anger, joy,
and interest. Affect, by contrast, is often conceptualized as varying along two dimensions,
either pleasantness and activation (Russell & Feldman Barrett, 1999) or positive and
negative emotional activation (Teilegen, Walson, & Clark, 1999).
Perspectives on the Functions of Affect and Emotions
Positive affect, according to numerous theorists, facilitates approach behavior (Cacioppo,
Gardner, & Berntson, 1999; Davidson, 1993; Watson, Wiese, Vaidya, & Teilegen, 1999) or
continued action (Carver & Scheier, 1990; Clore, 1994). From this perspective, experiences
of positive affect prompt individuals to engage with their environments and partake in
activities, many of which are adaptive for the individual, its species, or both. This link
between positive affect and activity engagement provides an explanation for She oftendocumented positivity offset, or the tendency for individuals to experience mild positive
affect frequently, even in neutral contexts (Diener & Diener, 1996; Ito & Cacioppo, 1999).
Without such an offset, individuals most often would be unmotivated to engage with their
environments. Yet with such an offset, individuals exhibit the adaptive bias to approach and
explore novel objects, people, or situations. (See Watson et al., 1999, for a related
explanation for diurnal patterns of positive emotional activation.)
Because positive emotions include a component of positive affect, they too function as
internal signals to approach or continue. Even so, positive emotions share this function with
a range of other positive affective states. Sensory pleasure, for instance, motivates people to
approach and continue consuming whatever stimulus is biologically useful for them at the
moment (Cabanac, 1971). Likewise, free-floating positive moods motivate people to
continue along any line of thinking or action that they have initiated (Clore, 1994). As such,
functional accounts of positive emotions that emphasize tendencies to approach or continue
may only capture the lowest common denominator across all affective states that share a
pleasant subjective feel, leaving additional functions unique to specific positive emotions
uncharted.
Discrete emotion theorists often link the function of specific emotions to the concept of
specific action tendencies (Frijda, 1986; Frijda, Kuipers, & Schure, 1989; Lazarus, 1991;
Levenson, 1994; Oatley & Jenkins, 1996; Tooby & Cosmides, 1990). Fear, for example, is
linked with the urge to escape, anger with the urge to attack, disgust with the urge to expel,
and so on. It is not that people invariably act out these urges when feeling particular
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emotions. Rather, people's ideas about possible courses of action narrow in on a specific set
of behavioral options. A key idea from this perspective is that a specific action tendency is
what makes an emotion evolutionarily adaptive: These are among the actions that
presumably worked best in helping human ancestors survive life-or-death situations (Tooby
& Cosmides, 1990). Another key idea from trie specific emotions perspective is that specific
action tendencies and physiological changes go hand in hand. So, for example, when
someone experiences an urge to escape when feeling fear, that person's body reacts by
mobilizing appropriate autonomic support for the possibility of running (Levenson, 1994).
Although specific action tendencies have been invoked to describe the function of specific
posiiive emotions as well, the action tendencies identified for positive emotions are notably
vague and underspecified (Fredrickson & Levenson, 1998). For instance, joy has been
linked with aimless activation, interest with attending, and contentment with inactivity
(Frijda, 1986). These tendencies are far too general to be called specific (Fredrickson, 1998).
They resemble generic urges to do anything or do nothing more than urges to do something
quite specific, like flee, attack, or spit. This is troublesome: If the action tendencies triggered
by positive emotions are vague, their effects on survival may be inconsequential. So, like the
view centered on generic approach tendencies, the view centered on specific action
tendencies yields an incomplete analysis of the function of positive emotions.
The Broaden-and-Build Theory of Positive Emotions
To advance understanding in this area, I formulated a new theoretical model to better
capture the unique effects of positive emotions. I call this the broaden-and-buiid theory of
posiiive emotions (Fredrickson, 1998). This theory states that certain discrete positive
emotions—including joy, interest, contentment, pride, and love—although
phenomenologically distinct, all share the ability to broaden people's momentary thoughtaction repertoires and build their enduring personal resources, ranging from physical and
intellectual resources to social and psychological resources.
I contrast this new theory (o traditional models based on specific action tendencies. Specific
action tendencies work well to describe the function of negative emotions and should be
retained for models of this subset of emotions. Without loss of theoretical nuance, a specific
action tendency can be redescribed as the outcome of a psychological process that narrows a
person's momentary thought–action repertoire by calling to mind an urge to act in a
particular way (e.g., escape, attack, expel). In a life-threatening situation, a narrowed
thought–action repertoire promotes quick and decisive action that carries direct and
immediate benefit. Specific action tendencies called forth by negative emotions represent
the sort of actions that likely worked best to save human ancestors' lives and limbs in similar
situations.
Although positive emotions can occur in adverse circumstances, the typical context of
positive emotions is not a life-threatening situation. As such, a psychological process that
narrows a person's momentary thought-action repertoire to promote quick and decisive
action may not be needed. Instead, the positive emotions of joy, interest, contentment, pride,
and love appear to have a complementary effect: They broaden people's momentary thoughtaction repertoires, widening the array of the thoughts and actions that come to mind
(Fredrickson, 1998; Fredrickson & Branigan, 2001). Conceptual analyses of a range of
positive emotions support this claim. Joy, for instance, broadens by creating the urge to play,
push the limits, and be creative. These urges are evident not only in social and physical
behavior, but also in intellectual and artistic behavior (Ellsworth & Smith, 1988; Frijda,
1986). Interest, a phenomenologically distinct positive emotion, broadens by creating the
urge to explore, take in new information and experiences, and expand the self in the process
Fredrickson
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(Csikszentmihalyi, 1990; Izard, 1977; Ryan & Deci, 2000; Tomkins, 1962). Contentment, a
third distinct positive emotion, broadens by creating the urge to savor current life
circumstances and integrate these circumstances into new views of self and of the world
(Izard, 1977). Pride, a fourth distinct positive emotion that follows personal achievements,
broadens by creating the urge to share news of the achievement with others and to envision
even greater achievements in the future (Lewis, 1993). Love, conceptualized as an amalgam
of distinct positive emotions (e.g., joy, interest, contentment) experienced within contexts of
safe, close relationships (Izard, 1977), broadens by creating recurring cycles of urges to play
with, explore, and savor experiences with loved ones. These various thought-action
tendencies—to play, to explore, to savor and integrate, or to envision future achievement—
each represent ways that positive emotions broaden habitual modes of thinking or acting
(Fredrickson, 1998,2000a; Fredrickson & Branigan, 2001).
In contrast to negative emotions, which carry direct and immediate adaptive benefits in
situations that threaten survival, the broadened thought-action repertoires triggered by
positive emotions are beneficial in other ways. Specifically, these broadened mindsets carry
indirect and long-term adaptive benefits because broadening builds enduring personal
resources, which function as reserves to be drawn on later to manage future threats.
Take play, the urge associated with joy, as an example. Animal research has found that
specific forms of chasing play evident in juveniles of a species, like running into a flexible
sapling or branch and catapulting oneself in an unexpected direction, are seen in adults of
that species exclusively during predator avoidance (Dolhinow, 1987). Such correspondences
suggest that juvenile play builds enduring physical resources (Boulton & Smith, 1992; Caro,
1988). Play also builds enduring social resources: Social play, with its shared amusement,
excitement, and smiles, builds lasting social bonds and attachments (Aron, Norman, Aron,
McKenna, & Heyman, 2000; Lee, 1983; Simons, McCluskey-Fawcett, & Papini, 1986),
which can become the locus of subsequent social support. Childhood play also builds
enduring intellectual resources by increasing levels of creativity (Sherrod & Singer, 1989),
creating theory of mind (Leslie, 1987), and fueling brain development (Panksepp, 1998).
Other positive emotions, like interest, contentment, pride, and love, similarly augment
individuals' personal resources, ranging from physical and social resources to intellectual
and psychological resources. (Fuller descriptions of the broaden-and-build theory are
available in Fredrickson, 1998, 2000a, in press; Fredrickson & Branigan, 2001.)
It is important to note that the personal resources accrued during states of positive emotions
are conceptualized as durable. They outlast the transient emotional states that led to their
acquisition. By consequence, then, the often incidental effect of experiencing a positive
emotion is an increase in one's personal resources. These resources function as reserves that
can be drawn on in subsequent moments and in different emotional states.
In short, the broaden-and-build theory describes the form of positive emotions in terms of
broadened thought–action repertoires and describes their function in terms of building
enduring personal resources. In doing so, the theory provides a new perspective on the
evolved adaptive significance of positive emotions. Human ancestors who succumbed to the
urges sparked by positive emotions to play, explore, and so on would have by consequence
accrued more personal resources. When these same ancestors later faced inevitable threats to
life and limb, their greater personal resources would have translated into greater odds of
survival, and, in turn, greater odds of living long enough to reproduce. To the extent, then,
that the capacity to experience positive emotions is genetically encoded, this capacity,
through the process of natural selection, would have become part of universal human nature.
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Evidence for the Broaden-and-Build Theory
Empirical support for several key propositions of the broaden-and-build theory can be drawn
from multiple sub-disciplines within psychology, ranging from cognition and intrinsic
motivation to attachment styles and animal behavior (for a review, see Fredrickson, 1998).
This evidence suggests that positive emotions broaden the scopes of attention, cognition,
and action and that they build physical, intellectual, and social resources. Yet much of this
evidence, because it predated the broaden-and-build theory, provides only indirect support
for the model. My collaborators and I have since initiated direct tests of hypotheses drawn
from the broaden-and-build theory. Although much work remains to be done, I briefly
describe our preliminary findings here. My hope is that this initial evidence will cultivate
interest among readers to conduct further studies on positive emotions that may serve to test
and refine the broaden-and-build theory (Fredrickson, 2000b).
Positive Emotions Broaden Thought-Action Repertoires
Foundational evidence for the proposition that positive emotions broaden people's
momentary thought-action repertoires comes from two decades of experiments conducted by
Isen and colleagues (for a review, see Isen, 2000). They have documented that people
experiencing positive affect show patterns of thought that are notably unusual (Isen,
Johnson, Mertz, & Robinson, 1985), flexible (Isen & Daubman, 1984), creative (Isen,
Daubman, & Nowicki, 1987), integrative (Isen, Rosenzweig, & Young, 1991), open to
information (Estrada, Isen, & Young, 1997), and efficient (Isen & Means, 1983; Isen et al.,
1991). They have also shown that those experiencing positive affect show an increased
preference for variety and accept a broader array of behavioral options (Kahn & Isen, 1993).
In general terms, Isen has suggested that positive affect produces a “broad, flexible cognitive
organization and ability to integrate diverse material” (Isen, 1990, p. 89), effects recently
linked to increases in brain dopamine levels (Ashby, Isen, & Turken, 1999). So although
Isen's work does not target specific positive emotions or thought-action tendencies per se, it
provides the strongest evidence that positive affect broadens cognition. Whereas negative
emotions have long been known to narrow people's attention, making them miss the forest
for the trees (or the suspect's style of dress for the gun), recent work suggests that positive
affect may expand attention (Derryberry & Tucker, 1994). The evidence comes from studies
that use global-local visual processing paradigms to assess biases in attentional focus.
Negative states—like anxiety, depression, and failure—predict local biases consistent with
narrowed attention, whereas positive states—like subjective well-being, optimism, and
success—predict global biases consistent with broadened attention (Basso, Schefft, Ris, &
Dember, 1996; Derryberry & Tucker, 1994).
These findings provide initial empirical footing for the hypothesis, drawn from the broadenand-build theory, that distinct types of positive emotions serve to broaden people's
momentary thought–action repertoires, whereas distinct types of negative emotions serve to
narrow these same repertoires. With Christine Branigan, I tested this broadening hypothesis
by showing research participants short, emotionally evocative film clips to induce the
specific emotions of joy, contentment, fear, and anger. We also used a nonemotional film
clip as a neutral control condition. Immediately following each film clip, we measured the
breadth of participants' thought–action repertoires. We asked them to step away from the
specifics of the film and imagine being in a situation in which similar feelings would arise.
We then asked them to list what they would like to do right then, given this feeling.
Participants recorded their responses on up to 20 blank lines that began with the phrase “I
would like to.”
Tallying the things each participant listed, Branigan and I found support for the broadening
hypothesis. Participants in the two positive emotions conditions (joy and contentment)
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identified more things that they would like to do right then relative to those in the two
negative emotion conditions (fear and anger) and, more important, relative to those in the
neutral control condition. Those in the two negative emotion conditions also named fewer
things than did those in the neutral control condition (Fredrickson & Branigan, 2000).
These data provide preliminary evidence that two distinct types of positive emotion—a high
activation state of joy and a low activation state of contentment—each produce a broader
thought-action repertoire than does a neutral state. Likewise, two distinct types of negative
emotion—fear and anger—each produce a narrower thought-action repertoire than does a
neutral state. This pattern of results supports a core proposition of the broaden-and-build
theory: that distinct positive emotions widen the array of thoughts and actions that come to
mind. By contrast, distinct negative emotions, as models based on specific action tendencies
would suggest, shrink this same array.
Despite this encouraging initial evidence, many questions arise: Do other positive and
negative emotions (e.g., interest, pride, love and sadness, disgust) conform to these effects?
Do the effects generalize to other measures of broadened cognition? If so, what basic
cognitive processes underlie this phenomenon? Do distinct positive emotions broaden (and
distinct negative emotions narrow) the scope of attention or the scope of working memory?
What are the neurological underpinnings? Are these effects mediated by changing levels of
circulating brain dopamine, as Ashby and colleagues (1999) have suggested? What brain
structures, circuits, and processes are involved? Finally, how are broadened thought–action
repertoires translated into decisions and action? These and other questions provide directions
for future work.
Positive Emotions Undo Lingering Negative Emotions
Evidence for the broadening hypothesis has clear implications for the strategies that people
use to regulate their experiences of negative emotions. If negative emotions narrow the
momentary thought–action repertoire and positive emotions broaden this same repertoire,
then positive emotions ought to function as efficient antidotes for the lingering effects of
negative emotions. In other words, positive emotions might correct or undo the after effects
of negative emotions; my colleagues and I call this the undoing hypothesis (Fredrickson &
Levenson, 1998; Fredrickson, Mancuso, Branigan, & Tugade, in press). The basic
observation that positive emotions (or key components of them) are somehow incompatible
with negative emotions is not new and has been demonstrated in earlier work on anxiety
disorders (e.g., systematic desensitization; Wolpe, 1958), motivation (e.g., opponent-process
theory; Solomon & Corbit, 1974), and aggression (e.g., principle of incompatible responses;
Baron, 1976). Even so, the precise mechanism ultimately responsible for this incompatibility
has not been adequately identified. The broadening function of positive emotions may play a
role. By broadening a person's momentary thought–action repertoire, a positive emotion may
loosen the hold that a negative emotion has gained on that person's mind and body by
dismantling or undoing preparation for specific action.
One marker of the specific action tendencies associated with negative emotions is increased
cardiovascular activity, which redistributes blood flow to relevant skeletal muscles. In the
context of negative emotions, then, positive emotions should speed recovery from or undo
this cardiovascular reactivity, returning the body to more midrange levels of activation. By
accelerating cardiovascular recovery, positive emotions create the bodily context suitable for
pursuing the broader array of thoughts and actions called forth.
My collaborators and I tested this undoing hypothesis by first inducing a high-activation
negative emotion in all participants (Fredrickson & Levenson, 1998; Fredrickson et al., in
press). In one study (Fredrickson et al., in press), we used a time-pressured speech
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preparation task. In just one minute, participants prepared a speech on the topic “Why you
are a good friend,” believing that their speech would be videotaped and evaluated by their
peers. This speech task induced the subjective experience of anxiety along with increases in
heart rate, peripheral vasoconstriction, and systolic and diastolic blood pressure. Into this
context of anxiety-related sympathetic arousal, we randomly assigned participants to view
one of four films. Two films elicited mild positive emotions (joy and contentment), and a
third served as a neutral control condition. Notably, these three films, when viewed
following a resting baseline, elicit virtually no cardiovascular reactivity (Fredrickson et al.,
in press). So the two positive-emotion films used in this study are indistinguishable from
neutrality with respect to cardiovascular changes. Our fourth film elicited sadness. We chose
sadness as an additional comparison because, among the negative emotions, it has not been
definitively linked to a high-energy action tendency, and thus it could be a contender for
speeding cardiovascular recovery.
The undoing hypothesis predicts that those who experience positive emotions on the heels of
a high-activation negative emotion will show the fastest cardiovascular recovery. My
colleagues and I tested this by measuring the time elapsed from the start of the randomly
assigned film until the cardiovascular reactions induced by the negative emotion returned to
baseline levels. In three independent samples, participants in the two positive emotion
conditions (joy and contentment) exhibited faster cardiovascular recovery than did those in
the neutral control condition. Participants in the sadness condition exhibited the most
protracted recovery (Fredrickson & Levenson, 1998; Fredrickson et al., in press).
Although the two positive-emotion films and the neutral film did not differ in what they do
to the cardiovascular system, these data suggest that they do differ in what they can undo
within this system. Two distinct types of positive emotions—mild joy and contentment—
share the ability to undo the lingering cardiovascular aftereffects of negative emotions.
Although the precise cognitive and physiological mechanisms of the undoing effect remain
unknown, the broaden-and-build theory suggests that broadening at the cognitive level
mediates undoing at the cardiovascular level. Phenomenologically, positive emotions may
help people place the events in their lives in broader context, lessening the resonance of any
particular negative event. Perhaps pointing to physiological markers of broadening effects,
some have suggested that parasympathetic cardiac control (measured as heart rate variability
or respiratory sinus arrhythmia) underlies positive emotions as well as the ability to regulate
negative emotions (Fox, 1989; McCraty, Atkinson, Tiller, Rein, & Watkins, 1995; Porges,
1995). Testing these suggestions and extending the work to other emotions and other
contexts provide a road map for future research.
Positive Emotions Fuel Psychological Resiliency
Evidence for the undoing effect of positive emotions suggests that people might improve
their psychological well-being, and perhaps also their physical health, by cultivating
experiences of positive emotions at opportune moments to cope with negative emotions
(Fredrickson, 2000a). Folkman and colleagues have made similar claims that experiences of
positive affect during chronic stress help people cope (Folkman, 1997; Folkman &
Moskowitz, 2000; Lazarus, Kanner, & Folkman, 1980). Evidence supporting this claim can
be drawn from experiments showing that positive affect facilitates attention to negative, selfrelevant information (Reed & Aspinwall, 1998; Trope & Neter, 1994; Trope & Pomerantz,
1998; for a review, see Aspinwall, 1998). Extrapolating from these findings, Aspinwall
(2001) described how positive affect and positive beliefs serve as resources for people
coping with adversity (see also Aspinwall & Taylor, 1997; Taylor, Kemeny, Reed, Bower,
& Gruenewald, 2000).
Fredrickson
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It seems plausible that some individuals, more than others, might intuitively understand and
use the benefits of positive emotions to their advantage. One candidate individual difference
is psychological resilience. Resilient individuals are said to bounce back from stressful
experiences quickly and efficiently, just as resilient metals bend but do not break (Carver,
1998; Lazarus, 1993). This theoretical definition of resilience suggests that, relative to their
less resilient peers, resilient individuals would exhibit faster cardiovascular recovery
following a high-activation negative emotion. Additionally, the broaden-and-build theory
suggests that this ability to bounce back to cardiovascular baseline may be fueled by
experiences of positive emotion.
With Michele Tugade, I tested these two hypotheses about resilient individuals using the
same time-pressured speech preparation task (described earlier) to induce a high-activation
negative emotion. We measured psychological resilience using Block and Kremen's (1996)
self-report scale. It is interesting to note that resilience did not predict the levels of anxiety
participants reported experiencing during the speech task or the magnitude of their
cardiovascular reactions to the stressful task, both of which were considerable. Resilience
did, however, predict participants' reports of positive emotions. Before the speech task was
even introduced, more resilient individuals reported higher levels of preexisting positive
affect on an initial mood measure. When later asked how they felt during the time-pressured
speech preparation phase, more resilient individuals reported that alongside their high
anxiety, they also experienced higher levels of happiness and interest.
As predicted by the theoretical definition of psychological resilience, more resilient
participants exhibited significantly faster returns to baseline levels of cardiovascular
activation following the speech task. Moreover, as predicted by the broaden-and-build
theory, this difference in time needed to achieve cardiovascular recovery was mediated by
differences in positive emotions (Tugade & Fredrickson, 2000).
These data suggest that positive emotions may fuel psychological resilience. In effect, then,
resilient individuals may be—wittingly or unwittingly—expert users of the undoing effect of
positive emotions. Again, questions arise from this initial study: Do resilient individuals
intentionally recruit positive emotions to cope? If so, how do they do it? Folkman and
Moskowitz (2000) identified three kinds of coping that can generate positive affect during
stressful circumstances: positive reappraisal, problem-focused coping, and the infusion of
ordinary events with positive meaning. Do resilient individuals use any or all of these
strategies? If so, can these strategies be taught to less resilient individuals? Finally, do
resilient individuals think more broadly, as the broaden-and-build theory would suggest? If
so, does broadened thinking enable people to find positive meaning within adversity? Again,
these remaining questions provide directions for future work.
Positive Emotions Build Psychological Resiliency and Trigger Upward Spirals Toward
Improved Emotional Well-Being
Preliminary evidence suggests that positive emotions may fuel individual differences in
resilience. Noting that psychological resilience is an enduring personal resource, the
broaden-and-build theory makes the bolder prediction that experiences of positive emotions
might also, over time, build psychological resilience, not just reflect it. That is, to the extent
that positive emotions broaden the scopes of attention and cognition, enabling flexible and
creative thinking, they should also augment people's enduring coping resources (Aspinwall,
1998, 2001; Isen, 1990). In turn, by building this psychological resource, positive emotions
should enhance people's subsequent emotional well-being. Consistent with this view, studies
have shown that people who experience positive emotions during bereavement are more
likely to develop long-term plans and goals. Together with positive emotions, plans and
goals predict greater psychological well-being 12 months postbereavement (Stein, Folkman,
Fredrickson
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Trabasso, & Richards, 1997; for related work, see Bonanno & Keltner, 1997; Keltner &
Bonanno, 1997). One way people experience positive emotions in the face of adversity is by
finding positive meaning in ordinary events and within the adversity itself (Affleck &
Tennen, 1996; Folkman & Moskowitz, 2000; Fredrickson, 2000a). It is important to note
that the relation between positive meaning and positive emotions is considered reciprocal:
Not only does finding positive meaning trigger positive emotion, but also positive emotions,
because they broaden thinking, should increase the likelihood of finding positive meaning in
subsequent events (Fredrickson, 2000a).
These suspected reciprocal relations among positive emotions, broadened thinking, and
positive meaning suggest that over time the effects of positive emotions should accumulate
and compound. The broadened attention and cognition triggered by earlier experiences of
positive emotion should facilitate coping with adversity, and this improved coping should
predict future experiences of positive emotion. As this cycle continues, people build their
psychological resilience and enhance their emotional well-being.
The cognitive literature on depression has already documented a downward spiral in which
depressed mood and the narrowed, pessimistic thinking it engenders influence one another
reciprocally, over time leading to ever-worsening moods and even clinical levels of
depression (Peterson & Seligman, 1984). The broaden-and-build theory predicts a
comparable upward spiral in which positive emotions and the broadened thinking they
engender also influence one another reciprocally, leading to appreciable increases in
emotional well-being over time. Positive emotions may trigger these upward spirals, in part
by building resilience and influencing the ways people cope with adversity. (For a
complementary discussion of upward spirals, see Aspinwall, 1998, 2001.)
With Thomas Joiner, I conducted an initial prospective test of the hypothesis that through
cognitive broadening, positive emotions produce an upward spiral toward enhanced
emotional well-being. We assessed positive and negative emotions, as well as a concept we
called broad-minded coping, at two time points, five weeks apart. Broad-minded coping was
tapped by items such as “think of different ways to deal with the problem” and “try to step
back from the situation and be more objective.”
Our data revealed clear evidence for an upward spiral. Individuals who experienced more
positive emotions than others became more resilient to adversity over time, as indexed by
increases in broad-minded coping. In turn, these enhanced coping skills predicted increased
positive emotions over time (Fredrickson & Joiner, 2000).
These findings suggest that positive emotions and broad-minded coping mutually build on
one another. Not only do positive emotions make people feel good in the present, but also,
through their effects on broadened thinking, positive emotions increase the likelihood that
people will feel good in the future. Because broad-minded coping is a form of psychological
resilience, these data are consistent with the prediction, drawn from the broaden-and-build
theory, that momentary experiences of positive emotion can build enduring psychological
resources and trigger upward spirals toward enhanced emotional well-being.
Again, many questions arise from these data. Does this upward spiral effect hold over longer
time intervals and across other measures of well-being and broadening? What are the
mechanisms of the effect? Do positive emotions beget future positive emotions because the
broadened thinking associated with earlier positive emotions helps people solve their
original problems, or because this broadened thinking enables people to find positive
meaning in other life circumstances and thus experience additional positive emotions?
Future studies, including experimental designs, are needed to answer these questions.
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Concluding Remarks
What role do positive emotions play within positive psychology? Traditional views would
suggest that experiences of positive emotion signal well-being and perhaps guide behavior
in the moment. Without minimizing the importance of these functions, the broaden-andbuild theory casts positive emotions in a much larger role. The theory suggests that positive
emotions, although fleeting, also have more long-lasting consequences. From the
perspective of the broaden-and-build theory, positive emotions are vehicles for individual
growth and social connection: By building people's personal and social resources, positive
emotions transform people for the better, giving them better lives in the future.
More specifically, the broaden-and-build theory suggests that multiple, discrete positive
emotions are essential elements of optimal functioning. As such, the capacities to experience
joy, interest, contentment, and love might be construed as fundamental human strengths that
yield multiple, interrelated benefits (Fredrickson, 2000e). My own research outlines a small
subset of these benefits. Specifically, I have shown that positive emotions (a) broaden
people's thought–action repertoires (Fredrickson & Branigan, 2000), (b) undo lingering
negative emotions (Fredrickson & Levenson, 1998; Fredrickson et al., in press), (c) fuel
psychological resilience (Tugade & Fredrickson, 2000), and (d) build psychological
resilience and trigger upward spirals toward enhanced emotional well-being (Fredrickson &
Joiner, 2000). Complementing this work, two new perspectives highlight the lasting
personal and social benefits of the positive emotions of gratitude (McCullough, Kil-patrick,
Emmons, & Larson, 2001; see also Fredrickson, 2000d) and elevation (Haidt, 2000). I hope
these initial findings inspire the further investigations of positive emotions that are needed to
test, refine, uphold, or discard the broaden-and-build theory, which in turn will advance
positive psychology.
One topic in particular need of study is the long-held but scantly supported hypothesis that
positive emotions foster physical health (for reviews, see Ryff & Singer, 1998; Salovey,
Rothman, Detweiler, & Steward, 2000). For instance, negative emotions, with their
heightened and often prolonged cardiovascular activation, have been implicated in the
etiology of coronary heart disease (Blascovich & Katkin, 1993; Fredrickson et al., 2000;
Williams, Barefoot, & Shekelle, 1985). If positive emotions shorten the duration of negative
emotional arousal, perhaps they may also slow the incremental progression toward disease
(Fredrickson & Levenson, 1998). Relaxation techniques are known to reduce blood pressure
in hypertensive adults (Blumenthal, 1985; Schneider et al., 1995), and they may do so
precisely because they capitalize on the broadening and undoing effects of contentment
(Fredrickson, 2000a). Additionally, Ryff and Singer (1998) suggested that physical health
depends on having quality connections to others and leading a life of purpose. Recent
evidence seems to support this assertion. For instance, people who consistently experienced
positive emotions with their parents as children and then later with their spouses as adults
were less than half as likely as others to exhibit high levels of cumulative wear and tear on
the body (Ryff, Singer, Wing, & Love, in press). Similarly, in a longitudinal study of 2,282
older Mexican Americans, those who reported high positive affect, compared with those
with less positive affect, were half as likely to have become disabled or to have died during
a two-year follow-up (Ostir, Markides, Black, & Goodwin, 2000). These new findings,
although somewhat isolated, underscore the message that positive emotions may be essential
for optimizing both psychological and physical functioning (Fredrickson, 2000a).
Yet the benefits of positive emotions identified thus far are likely just the tip of the
proverbial iceberg. As the positive psychology movement inspires additional research on
positive emotions, even more reasons to cultivate positive emotions may be discovered.
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Acknowledgments
My research on positive emotions is supported by Grants MH53971 and MH59615 from the National Institute of
Mental Health, a Rackham Faculty Grant and Fellowship from the University of Michigan, and funds from the John
Templeton Foundation.
Biography
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Psychological Bulletin
2005, Vol. 131, No. 6, 803– 855
Copyright 2005 by the American Psychological Association
0033-2909/05/$12.00 DOI: 10.1037/0033-2909.131.6.803
The Benefits of Frequent Positive Affect:
Does Happiness Lead to Success?
Sonja Lyubomirsky
Laura King
University of California, Riverside
University of Missouri—Columbia
Ed Diener
University of Illinois at Urbana–Champaign and The Gallup Organization
Numerous studies show that happy individuals are successful across multiple life domains, including
marriage, friendship, income, work performance, and health. The authors suggest a conceptual model to
account for these findings, arguing that the happiness–success link exists not only because success makes
people happy, but also because positive affect engenders success. Three classes of evidence— crosssectional, longitudinal, and experimental—are documented to test their model. Relevant studies are
described and their effect sizes combined meta-analytically. The results reveal that happiness is associated with and precedes numerous successful outcomes, as well as behaviors paralleling success.
Furthermore, the evidence suggests that positive affect—the hallmark of well-being—may be the cause
of many of the desirable characteristics, resources, and successes correlated with happiness. Limitations,
empirical issues, and important future research questions are discussed.
Keywords: happiness, subjective well-being, positive affect, positive emotions, meta-analysis
(1999) reviewed the correlations between happiness and a variety
of resources, desirable characteristics, and favorable life circumstances. Although the authors recognized that the causality can be
bidirectional, they frequently used wording implying that cause
flows from the resource to happiness. For example, they suggested
that marriage might have “greater benefits for men than for
women” (p. 290), apparently overlooking the possibility that sex
differences in marital patterns could be due to differential selection
into marriage based on well-being. Similarly, after reviewing links
between money and well-being, Diener and his colleagues pointed
out that “even when extremely wealthy individuals are examined,
the effects [italics added] of income are small” (p. 287), again
assuming a causal direction from income to happiness. We use
quotes from one of us to avoid pointing fingers at others, but such
examples could be garnered from the majority of scientific publications in this area. The quotes underscore the pervasiveness of the
assumption among well-being investigators that successful outcomes foster happiness. The purpose of our review is not to
disconfirm that resources and success lead to well-being—a notion
that is likely valid to some degree. Our aim is to show that the
alternative causal pathway—that happy people are likely to acquire favorable life circumstances—is at least partly responsible
for the associations found in the literature.
“A merry heart goes all the day, Your sad tires in a mile-a.”
—William Shakespeare
“The joyfulness of a man prolongeth his days.”
—Sirach 30:22
“The days that make us happy make us wise.”
—John Masefield
Research on well-being consistently reveals that the characteristics and resources valued by society correlate with happiness. For
example, marriage (Mastekaasa, 1994), a comfortable income
(Diener & Biswas-Diener, 2002), superior mental health
(Koivumaa-Honkanen et al., 2004), and a long life (Danner, Snowdon, & Friesen, 2001) all covary with reports of high happiness
levels. Such associations between desirable life outcomes and
happiness have led most investigators to assume that success
makes people happy. This assumption can be found throughout the
literature in this area. For example, Diener, Suh, Lucas, and Smith
Sonja Lyubomirsky, Department of Psychology, University of California, Riverside; Laura King, Department of Psychological Sciences, University of Missouri—Columbia; Ed Diener, Department of Psychology,
University of Illinois at Urbana–Champaign and The Gallup Organization,
Omaha, Nebraska.
This work was supported in part by grants from the Positive Psychology
Network. We are grateful to Fazilet Kasri, Rene Dickerhoof, Colleen
Howell, Angela Zamora, Stephen Schueller, Irene Chung, Kathleen Jamir,
Tony Angelo, and Christie Scollon for conducting library research and
especially to Ryan Howell for statistical consulting.
Correspondence concerning this article should be addressed to Sonja
Lyubomirsky, Department of Psychology, University of California, Riverside, CA 92521. E-mail: [email protected]
A PRELIMINARY CONCEPTUAL MODEL
In this article, we review evidence suggesting that happy people—those who experience a preponderance of positive emotions—tend to be successful and accomplished across multiple life
domains. Why is happiness linked to successful outcomes? We
propose that this is not merely because success leads to happiness,
but because positive affect (PA) engenders success. Positively
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LYUBOMIRSKY, KING, AND DIENER
804
valenced moods and emotions lead people to think, feel, and act in
ways that promote both resource building and involvement with
approach goals (Elliot & Thrash, 2002; Lyubomirsky, 2001). An
individual experiencing a positive mood or emotion is encountering circumstances that he or she interprets as desirable. Positive
emotions signify that life is going well, the person’s goals are
being met, and resources are adequate (e.g., Cantor et al., 1991;
Carver & Scheier, 1998; Clore, Wyer, Dienes, Gasper, & Isbell,
2001). In these circumstances, as Fredrickson (1998, 2001) has so
lucidly described, people are ideally situated to “broaden and
build.” In other words, because all is going well, individuals can
expand their resources and friendships; they can take the opportunity to build their repertoire of skills for future use; or they can
rest and relax to rebuild their energy after expending high levels of
effort. Fredrickson’s model (Fredrickson, 2001) suggests that a
critical adaptive purpose of positive emotions is to help prepare the
organism for future challenges. Following Fredrickson, we suggest
that people experiencing positive emotions take advantage of their
time in this state—free from immediate danger and unmarked by
recent loss—to seek new goals that they have not yet attained (see
Carver, 2003, for a related review).
The characteristics related to positive affect include confidence,
optimism, and self-efficacy; likability and positive construals of
others; sociability, activity, and energy; prosocial behavior; immunity and physical well-being; effective coping with challenge and
stress; and originality and flexibility. What these attributes share is
that they all encourage active involvement with goal pursuits and
with the environment. When all is going well, a person is not well
served by withdrawing into a self-protective stance in which the
primary aim is to protect his or her existing resources and to avoid
harm—a process marking the experience of negative emotions.
Positive emotions produce the tendency to approach rather than to
avoid and to prepare the individual to seek out and undertake new
goals. Thus, we propose that the success of happy people rests on
two main factors. First, because happy people experience frequent
positive moods, they have a greater likelihood of working actively
toward new goals while experiencing those moods. Second, happy
people are in possession of past skills and resources, which they
have built over time during previous pleasant moods.
This unifying framework builds on several earlier bodies of
work—the broaden-and-build model of positive emotions
(Fredrickson, 1998, 2001), the notion that positive emotions convey specific information to the person (Ortony, Clore, & Collins,
1988), the idea of positivity offset (Ito & Cacioppo, 1999), work
on the approach-related aspects of PA (Watson, 2000), and, finally, Isen’s (e.g., 2000) groundbreaking research on the behaviors
that follow positive mood inductions. We extend the earlier work
in predicting that chronically happy people are in general more
successful, and that their success is in large part a consequence of
their happiness and frequent experience of PA. Although the vast
majority of research on emotions has been on negative states, a
body of literature has now accumulated that highlights the importance of positive emotions in people’s long-term flourishing.
Classes of Evidence
Figure 1 displays our general conceptual model, which proposes
that successful outcomes are caused by happiness and do not
merely correlate with it or follow from it. Specifically, below the
conceptual model, we display four classes of evidence that can be
used to test it. The first type of evidence (Type A) represents
positive correlations derived from cross-sectional studies. Although it is a truism that correlation does not imply causation,
correlations must generally be positive to be consistent with propositions about causality. Except in the rare case in which strong
third-variable suppressor effects exist across studies, an absence of
correlation between two variables indicates an absence of causality
in either direction. Thus, correlational evidence is germane to our
argument because the absence of positive correlations suggests
that happiness does not cause success.
The second class of evidence (Type B) is based on longitudinal
research, and is somewhat more informative about causal direction
than cross-sectional correlations. If one variable precedes another
in time and other potential causal variables are statistically controlled, the resulting causal model can be used to reject a causal
hypothesis. In cases in which changes in variable X are shown to
precede changes in variable Y, this form of evidence is even more
strongly supportive of a causal connection, although the influence
of third variables might still contaminate the conclusions and leave
the direction of cause in doubt. Evidence of Type C, the classic
laboratory experiment, is commonly believed to represent the
strongest evidence for causality, although even in this case it can
be difficult to determine exactly what aspect of the experimental
manipulation led to changes in the dependent variable. Finally,
long-term experimental intervention studies (Type D evidence)
would offer the strongest test of our causal model, although again
the active ingredients in the causal chain are usually not known
with certainty.
Empirical Tests of Model and Organizational Strategy
Because no single study or type of evidence is definitive, an
argument for causality can best be made when various classes of
evidence all converge on the same conclusion. Therefore, we
document several types of evidence in our article in order to most
rigorously test the idea that happiness leads to success. Our review
covers the first three classes of evidence (Types A, B, and C) and
is organized around five focal questions arising from these three
categories:
1.
Cross-sectional studies (Type A)
Question 1: Are happy people successful people?
Question 2: Are long-term happiness and short-term
PA associated with behaviors paralleling success—
that is, with adaptive characteristics and skills?
2.
Longitudinal studies (Type B)
Question 3: Does happiness precede success?
Question 4: Do happiness and positive affect precede
behaviors paralleling success?
3.
Experimental studies (Type C)
Question 5: Does positive affect lead to behaviors
paralleling success?
First, we document the extensive cross-sectional correlational
evidence (Type A), as shown in Figure 1. The first question
addressed by this evidence is the one that forms the basis of our
causal hypothesis—that is, are happy people more likely to suc-
BENEFITS OF FREQUENT POSITIVE AFFECT
Figure 1.
805
Empirically testing the conceptual model. PA ⫽ positive affect; Grp. ⫽ group.
ceed at culturally valued goals (e.g., concerning work, love, and
health) than their less happy peers? However, the large number of
available correlational studies in this category also includes relevant research examining behavior and cognition that parallel successful life outcomes—that is, the characteristics, resources, and
skills that help people succeed (e.g., attributes such as selfefficacy, creativity, sociability, altruism, immunity, and coping).
Accordingly, the second question addressed by this evidence explores the relations of behavior paralleling success to long-term
happiness and short-term PA. Because we define happiness as the
806
frequent experience of positive emotions over time (see below),
our model assumes that the correlations involving long-term happiness are parallel to those of short-term positive moods. In conclusion, only if the correlations generated by Questions 1 and 2 are
generally positive will we consider our causal hypothesis further.
Second, we consider longitudinal studies, which address two
further questions. Is happiness at Time 1 associated with successful outcomes at Time 2 (Question 3)? Is happiness and PA at Time
1 correlated with behaviors paralleling success at Time 2 (Question 4)? In summary, prior levels of happiness and positive affect
must correlate with later levels of successful outcomes and behavior for our causal hypothesis not to be rejected.
In laboratory experimentation, the third type of evidence, causality is put to a stronger test. In this case, however, because of the
limits of the laboratory, only short-term changes in behavior and
cognitions that parallel successful life outcomes are assessed.
Thus, the fifth and final question we address is whether PA causes
the cognitive and behavioral characteristics paralleling success.
Again, because positive affect is defined here as the basic constituent of happiness, our model requires that the outcomes of shortterm positive moods are parallel to the successful outcomes in our
conceptual model. Furthermore, this question is critical, as it
speaks to whether PA may be a mediator underlying the relationship between happiness and flourishing—that is, whether PA
causes the adaptive characteristics that help happy people succeed.
Although the fourth type of evidence shown in Figure 1 (Type
D) would provide the strongest type of data for our model, unfortunately, to our knowledge no studies of this type exist. Nevertheless, support for our conceptual model from all three of the
previously described types of evidence, while not definitive, will
suggest a likelihood that our causal model is correct. Furthermore,
combining the three types of evidence represents an advance
beyond laboratory experimentation alone, because the relatively
greater rigor and control provided by experimentation are supplemented by the relatively greater ecological validity provided by the
other types of studies. Thus, the first two classes of evidence
(Types A and B) speak to the plausibility of generalizing the causal
laboratory findings to the context of success and thriving in everyday life. Meanwhile, by revealing the processes uncovered in
the laboratory, the experimental evidence (Type C) illuminates the
possible causal sequence suspected in the correlational data. Taken
together, consistent findings from all three types of data offer a
stronger test than any single type of data taken alone.
After describing our methodology and defining our terms, we
address each of the five focal questions in order, documenting the
three classes (A, B, and C) of relevant empirical evidence. Then,
we turn to a discussion of several intriguing issues and questions
arising out of this review, caveats and limitations, and important
further research questions.
Methodological Approach
To identify the widest range of published papers and dissertations, we used several search strategies (Cooper, 1998). First, we
searched the PsycINFO online database, using a variety of key
words (e.g., happiness, satisfaction, affect, emotion, and mood).
Next, using the ancestry method, the reference list of every empirical, theoretical, and review paper and chapter was further
combed for additional relevant articles. To obtain any papers that
might have been overlooked by our search criteria, as well as to
locate work that is unpublished or in press, we contacted two large
electronic listserves, many of whose members conduct research in
the area of well-being and emotion—the Society of Personality
and Social Psychology listserv and the Quality of Life Studies
listserv. Twenty-four additional relevant articles were identified
with this method.
The final body of literature was composed of 225 papers, of
which 11 are unpublished or dissertations. From these 225 papers,
we examined 293 samples, comprising over 275,000 participants,
and computed 313 independent effect sizes. A study was included
in our tables if it satisfied the following criteria. First, measures of
happiness, PA, or a closely related construct had to be included, in
addition to assessment of at least one outcome, characteristic,
resource, skill, or behavior. Second, the data had to include either
a zero-order correlation coefficient or information that could be
converted to an r effect size (e.g., t tests, F tests, means and
standard deviations, and chi-squares). If a study did not report an
r effect size, we computed one from descriptive statistics, t statistics, F ratios, and tables of counts (see Rosenthal, 1991). If no
relevant convertible statistics were presented, other than a p value,
we calculated the t statistic from the p value and an
r-sub(equivalent) (Rosenthal & Rubin, 2003). When a paper reported p ⬍ .05, p ⬍ .01, or ns, we computed rsub(equivalent) with
p values of .0245, .005, and .50 (one-tailed), respectively, which
likely yielded a highly conservative estimate of the effect size.
Finally, the sample size had to be available. When possible, we
also contacted authors for further information.
Descriptions of the critical elements of each study (i.e., authors,
year, sample size, happiness/PA measure or induction, related
construct, and effect size [r]) are included in Tables 1, 2, and 3,
which present cross-sectional, longitudinal, and experimental
work, respectively. Table 2 additionally presents the length of time
between assessments, and Table 3 includes the comparison groups
used in the studies. Studies with subscripts after their name are
those that appear in more than a single section or table, usually
because multiple outcome variables are included.
Furthermore, mirroring our documentation of the literature presented in this paper, Tables 1–3 are subdivided into substantive
categories (or panels). For example, Table 1 is subdivided into
nine categories—work life, social relationships, health, perceptions of self and others, sociability and activity, likability and
cooperation, prosocial behavior, physical well-being and coping,
and, finally, problem solving and creativity. The mean and median
effect size (r), weighted and unweighted by sample size, as well as
a test of heterogeneity, is provided for each category for the three
classes of data (cross-sectional, longitudinal, and experimental) in
Table 4.
Tables 1, 2, and 3 report all effect sizes of interest to readers—
including instances of two or more effect sizes generated from the
same sample or dataset. For example, the relation of happiness
with income and marital status derived from a single study may
appear in two different panels of a table (i.e., work life and social
relationships). Alternatively, the correlation between happiness
and coping derived from a single longitudinal study may appear in
two different tables (e.g., the cross-sectional table and the longitudinal table). However, in order to meta-analytically combine the
464 effect sizes listed in Tables 1–3, we had to ensure a degree of
(text continues on page 816)
807
Table 1
Study Information and Effect Sizes for Nine Categories of Cross-Sectional Research
Study
n
Happiness/PA measure
Effect size
(r)
Correlated construct
Work life
Credé et al., 2005
Cropanzano & Wright, 1999a
(first assessment)
(second assessment)
DeLuga & Mason, 2000
Donovan, 2000
Donovan, 2000
Donovan, 2000
Donovan, 2000
Donovan, 2000
Foster et al., 2004
Foster et al., 2004
Frisch et al., 2004
George, 1989
George, 1995
George, 1995
Graham et al., in pressa (1995 assessment)
Howell et al., in press
Jundt & Hinsz, 2001
Krueger et al., 2001a
Lucas et al., 2004
Magen & Aharoni, 1991a
Miles et al., 2002
Seligman & Schulman, 1986a (Study 1)
Staw & Barsade, 1993a
959
959
959
60
PANAS
PANAS
PANAS
Index of Psychological Well-Being
Organizational citizenship behavior
Counterproductive work behavior
Job withdrawal
Supervisory evaluations
60
Affectometer 2
Current Mood Report
Current Mood Report
Current Mood Report
Current Mood Report
Current Mood Report
Job Affect Scale
Job Affect Scale
Quality of Life Inventory
Job Affect Scale
PANAS (leader)
PANAS (aggregated group)
One-item happiness
One-item happiness
SWLS
Seven-point semantic differentials
MPQ positive emotionality
One-item happiness
Four-item positive affect
Four-item positive affect
Job-Related Affective Well-Being
Scale
Attributional Style Questionnaire
Three-measure composite of positive
affectivity
Experience and expression of
positive emotion on the job
One-item happiness
One-item happiness (12 times over
4 days)
PANAS
PANAS
Fordyce HM Scale
PANAS
Job performance
Turnover intentions
Work withdrawal
Organizational retaliatory behavior
Satisfaction with work
Organizational climate for performance
Employee health and well-being
Academic retention absenteeism
92
188
188
188
188
188
41
41
3,638
254
53
53
4,524
5,134
307
164
397
24,000
260
260
203
94
83
Staw et al., 1994a
272
Staw et al., 1994a
272
Staw et al., 1994a
272
Thoits & Hewitt, 2001a
Totterdell, 2000*
Van Katwyk et al., 2000a (Study 3)
Weiss et al., 1999a
Wright & Cropanzano, 1998
Wright & Cropanzano, 2000
(Study 1)
Wright & Cropanzano, 2000 (Study 2)
Wright & Staw, 1999a (Study 1,
second assessment)
first assessment)
second assessment)
3,617
17
111
111
24
52
47
Judged customer service
Judged customer service
Income
Income
Material wealth
Task performance
Self-reported altruism
Income
Transpersonal commitment
Involvement in community service
.37
⫺.25
⫺.25
.29
.34
.22
.20
⫺.38
⫺.20
⫺.22
.50
.32
.29
.18
⫺.28
.41
.35
.20b
.16b
.23
.19
.44
.20
.21
.36
.23
Quarterly insurance commissions
Judged managerial performance
.18
.20
Job autonomy, meaning, and variety
.22
Gross annual salary
.12
Supervisory evaluations (creativity)
.30
Time spent volunteering
Cricket batting average
.09
.36
Interpersonal conflict
Intention to quit
Job satisfaction
Emotional exhaustion
Job performance
⫺.12
⫺.33
.29
⫺.39
.32
37
45
.34
.33
62
.25
64
.43
Social relationships
Baldassare et al., 1984
Berry & Willingham, 1997
Cooper et al., 1992a (Study 1 & Study 2)
Cooper et al., 1992a (Study 2)
202
202
202
127
118
118
Four-item happiness
Four-item happiness
Four-item happiness
PANAS
SWLS
SWLS
Instrumental support
Emotional support
Companionship
Commitment to current relationship
Satisfaction with friends
Satisfaction with social activities
.17
.15
.30
.27
.31
.37
(table continues)
808
Table 1 (continued )
Study
n
Effect size
(r)
Social relationships (continued)
Diener & Seligman, 2002a
Diener et al., 2000
Gladow & Ray, 1986a
Gladow & Ray, 1986a
Glenn & Weaver, 1981a (Black female
sample)
Glenn & Weaver, 1981a (Black male
sample)
Glenn & Weaver, 1981a (White female
sample)
Glenn & Weaver, 1981a (White male
sample)
Headey et al., 1991a (1981 assessment)
Kozma & Stones, 1983
Lee & Ishii-Kuntz, 1987 (male sample)
Lee & Ishii-Kuntz, 1987 (male sample)
Lee & Ishii-Kuntz, 1987 (female sample)
Lee & Ishii-Kuntz, 1987 (female sample)
Lyubomirsky et al., in pressa
Mastekaasa, 1994
Mishra, 1992a
Mroczek & Spiro, 2005a
Pfeiffer & Wong, 1989a
Phillips, 1967* (healthy sample)
Requena, 1995 (Spanish sample)
Requena, 1995 (U.S. sample)
Ruvolo, 1998a (husbands sample)
Ruvolo, 1998a (husbands sample)
Ruvolo, 1998a (wives sample)
Ruvolo, 1998a (wives sample)
Stack & Eshleman, 1998 (male sample)
Stack & Eshleman, 1998 (female sample)
Staw et al., 1994a
Strayer, 1980a
106
SWLS, affect balance, memory
recall
One-item life satisfaction
One-item happiness
One-item happiness
One-item happiness
Relationshipswithclosefriends
.48
Marital status
Support from friends
Support from neighbors
Marital happiness
.07b
.35
.31
.18
167
One-item happiness
Marital happiness
.22
820
One-item happiness
Marital happiness
.53
1,872
One-item happiness
Marital happiness
.37
One-item happiness
One-item happiness
Life-as-a-Whole Index
MUNSH
Seven-item morale
Seven-item morale
Seven-item morale
Seven-item morale
SHS
SHS
Bradburn’s Scales, one-item life
satisfaction, one-item happiness
Index of Life Satisfaction
Marital status
Marital status
Satisfaction with marriage
Marital status
No. of close friends
Loneliness
No. of close friends
Loneliness
Satisfaction with recreation
Marital status
59,169
63
63
89
4,524
5,134
649
649
649
649
600
1,321
1,321
1,551
1,551
621
621
25,810
720
1,927
59
430
1,084
1,534
317
317
317
317
9,237
10,127
272
14
Life Satisfaction Inventory
MUNSH
One-item happiness
One-item happiness
One-item happiness
One-item happiness
One-item happiness
One-item happiness
One-item happiness
One-item happiness
One-item happiness
Observational count of happy affect
Willi, 1997
383
Relationship-relevant happiness
Achat et al., 2000a
Bogner et al., 2001
Chang & Farrehi, 2001
Chang & Farrehi, 2001
Collins et al., 1992
659
168
402
402
73
106
106
106
LOT
SWLS
LOT-Revised
SWLS
MAACL-Revised
recall
recall
recall
Daily Mood Scale
Daily Mood Scale
Daily Mood Scale
Daily Mood Scale
Daily Mood Scale
Daily Mood Scale
Social interactions with nonfamily
members
Marital status
Jealousy in specific relationship
Social participation
No. of friends
No. of friends
Marital well-being
Spouse’s marital well-being
Marital well-being
Marital status
Marital status
Emotional and tangible support from
supervisors
Observational count of empathic
responses to others
Extent in love with partner
.03b
.02b
.47
.55
.49
.47
.20
.23
⫺.50
.19
⫺.51
.50
.51
.29
.41
.23
⫺.03
.17
.13
.08
.12
.16
.41
.34
.15b
.16b
.33
.59
.19
Health
Gil
Gil
Gil
Gil
Gil
Gil
et
et
et
et
et
et
al.,
al.,
al.,
al.,
al.,
al.,
2004a
2004a
2004a
2004a
2004a
2004a
41
41
41
41
41
41
Vitality
History of substance abuse
Depressive symptoms
Depressive symptoms
Quality of life
Depression
.14b
⫺.27
⫺.36
⫺.57
.32
⫺.61
Hypochondriasis
⫺.24
Schizophrenia
⫺.53
Pain
ER visits
Hospital visits
Doctor calls
Medication use
Work absences
⫺.42
⫺.06b
⫺.06b
⫺.08b
⫺.08b
⫺.09b
809
Study
n
Effect size
(r)
Health (continued)
Kashdan & Roberts, 2004a
Kehn, 1995a
Laidlaw et al., 1996
Lobel et al., 2000
Lu & Shih, 1997
Mroczek & Spiro, 2005a (1978-1980
sample)
Mroczek & Spiro, 2005a (1981-1983
sample)
Mroczek & Spiro, 2005a (1984-1986
sample)
Mroczek & Spiro, 2005a (1987-1989
sample)
Mroczek & Spiro, 2005a (1990-1992
sample)
Mroczek & Spiro, 2005a (1993-1995
sample)
Mroczek & Spiro, 2005a (1996-1998
sample)
Mroczek & Spiro, 2005a (1999-2000
sample)
Phillips, 1967a
Røysamb et al., 2003a
Windle, 2000a
⫺.03b
⫺.05b
⫺.34
.43
⫺.33
⫺.20
⫺.36
.43
⫺.29
⫺.49
.23
4,524
5,134
104
98
38
129
191
621
621
621
1,254
One-item happiness
One-item happiness
PANAS
Life Satisfaction Index
One-item peacefulness
LOT
Chinese Happiness Inventory
SHS
SHS
SHS
Health problems
Health problems
Social phobia/anxiety
Global health
Size of allergic reaction
Delivery of low-birth-weight infants
Poor mental health
Satisfaction with health
Physical symptoms
Depressed affect
Global health
1,267
Global health
.31
1,283
Global health
.31
1,641
Global health
.24
965
Global health
.26
974
Global health
.29
919
Global health
.29
389
Global health
.34
One-item happiness
SWB Index
SWB Index
Revised Dimension of Temperament
Survey
Overall mental health
Global health
Musculoskeletal pain
Delinquent activity
.22
.50
⫺.25
⫺.22
PANAS
SWLS
PANAS
SWLS
PANAS
Inventory of Personal Happiness
One-item happiness
One-item happiness
One-item happiness
Quality of conversation
Satisfaction with relatives
Satisfaction with relatives
Hostility toward other women
Support received from friends
Support received from relatives
Satisfaction with friendships
.27
.22
.12
.31
.23
⫺.21
.35
.14
.22
167
One-item happiness
.23
820
One-item happiness
.29
89
One-item happiness
.13
1,872
One-item happiness
Satisfaction with family life
.25
167
One-item happiness
.15
820
One-item happiness
.39
89
One-item happiness
.17
Neutral Objects Satisfaction
Questionnaire
Neutral Objects Satisfaction
Questionnaire
SWLS
SWLS
Judged favorability of reference letter
(hypothetical)
Judged favorability of reference letter
(actual)
Self-esteem
Optimism
.29
593
6,576
6,576
1,016
Positive perceptions of self and others
Berry & Hansen, 1996a (Study 1)
Cowan et al, 1998
Gladow & Ray, 1986a
Gladow & Ray, 1986a
sample)
sample)
sample)
sample)
sample)
sample)
sample)
sample)
Judge & Higgins, 1998 (Study 1)
Judge & Higgins, 1998 (Study 2)
Lucas et al., 1996 (Study 1)
112
118
118
118
118
90
63
63
1,872
110
95
212
212
.17
.59
.60
(table continues)
810
Study
n
Effect size
(r)
Positive perceptions of self and others (continued)
Lyubomirsky & Tucker, 1998a (Study 1)
Mayer et al., 1988 (preliminary study)
Mayer et al., 1988 (Study 2)
Mongrain & Zuroff, 1995
Ryff, 1989
Schimmack et al., 2004a (Study 1)
Tarlow & Haaga, 1996
Totterdell, 2000a
Weiss et al., 1999a
109
109
172
172
621
621
621
621
621
621
621
621
621
621
621
105
47
38
206
193
152
123
123
123
321
136
124
136
124
18
24
SWLS
SWLS
SWLS
SWLS
SHS
SHS
SHS
SHS
SHS
SHS
SHS
SHS
SHS
SHS
SHS
SHS
SHS
SHS
Mood-State Introspection Scale
Mood-State Introspection Scale
Four positive adjectives
MUNSH
MUNSH
MUNSH
SWLS
SWLS
SWLS
PANAS
One-item happiness (12 times over
4 days)
Fordyce HM Scale
Self-esteem
Optimism
Self-esteem
Optimism
Self-esteem
Optimism
Sense of mastery
Perceived control
Satisfaction with family relations
Satisfaction with education
Satisfaction with housing
Satisfaction with transportation
Evaluations of past life events
Liking of videotaped target
Evaluations of real-life target
Inferences about people
Inferences about people
Self-criticism
Cognitive jealousy
Emotional jealousy
Behavioral jealousy
Personal growth
Self-rated assertiveness
Self-rated assertiveness
Self-rated warmth
Self-esteem
Self-rated performance
.65
.59
.54
.57
.62
.60
.55
.47
.41
.50
.43
.27
.51
.43
.34
.41
.29
.36
.29
.29
⫺.39
⫺.08
⫺.24
⫺.17
.38
.21
.36
.27
.57
.50
Satisfaction with job
.29
Attendance at club meetings
Quality of conversation
Degree of disclosure in conversation
Degree of engagement in conversation
Intimacy of conversation
No. of daily interactions
Extraversion
Extraversion
.31
.27
.06
.10
.09
.34
.31
.43
Extraversion
Extraversion
Social activities
Extraversion
Extraversion
Informant-rated energy
Extraversion
.21
.54
.40
.16
.16
.39
.49
Peer ratings of target’s relationships
.65
Performance-approach goals
Personal conversations
Intrinsically motivating
experiences (%)
Extraversion
Self-rated affiliation
Observer-rated affiliation
Extraversion
Extraversion
Flow
Satisfaction with activities
.15
.35
.28
Sociability and activity
Bahr & Harvey, 1980
Berry & Hansen, 1996a
Brebner et al., 1995
(Study
(Study
(Study
(Study
(Study
1)
1)
1)
1)
2)
44
112
112
112
112
105
95
95
Burger & Caldwell, 2000a
Costa & McCrae, 1980a
Diener & Fujita, 1995a
95
134
134
753
554
186
106
106
Elliot & Thrash, 2002
Gladow & Ray, 1986a
Graef et al., 1983
176
63
107
Griffin et al., in press
Harker & Keltner, 2001a
Headey & Wearing, 1989
Headey & Wearing, 1989
Hektner, 1997a
Kahana et al., 1995
1,051
49
114
649
649
281
257
One-item happiness
PANAS
PANAS
PANAS
PANAS
PANAS
Oxford Happiness Inventory
Personal State Questionnaire,
Version 5
LOT
PANAS
PANAS
Bradburn’s Scales
Bradburn’s Scales
SWLS
recall
recall
General Temperament Survey
One-item happiness
One-item happiness
PANAS
FACS Duchenne smile
FACS Duchenne smile
Bradburn’s Scales
One-item happy mood
Fifteen items from the 22-item
screening score
.32
.33
.69
.20
.18
.27
.38
811
Study
n
Effect size
(r)
Sociability and activity (continued)
Lu & Argyle, 1991
Lu & Argyle, 1991
Lucas et al., 2000
Lucas et al., 2000
Lucas et al., 2000
Lucas, 2001a (daily study)
Lucas, 2001a (moment study)
Matikka & Ojanen, in press
Matikka & Ojanen, in press
Mishra, 1992a
104
104
114
114
5,842
5,842
5,842
144
144
144
124
124
621
621
376
376
720
Mishra, 1992a
Mishra, 1992a
Schimmack et al., 2004a (Study
Stones & Kozma, 1986a
Watson, 1988a
Watson et al., 1992a (Study 1)
1)
1)
1)
2)
2)
PANAS
PANAS
PANAS
PANAS
PANAS
PANAS
PANAS
PANAS
Time felt happy and pleasant (%)
SHS
SHS
Three-item happiness
720
720
136
136
136
124
124
408
71
85
127
79
SWLS
SWLS
SWLS
SWLS
SWLS
MUNSH
Positive Emotionality Scale
PANAS (weekly, over 13 weeks)
PANAS (daily, over 6–7 weeks)
PANAS, extraversion, positive
temperament
PANAS, joviality
PANAS, extraversion, positive
temperament
96
120
Attraction to partner
Closeness to partner
Attitude toward joint activities
Attitude toward group activities
Extraversion
Ascendance
Affiliation
Experience of Affiliation/warmth
Time spent with friends
Time spent leading
Time spent leading
Time spent with friends and family
Extraversion
Social participation
Social inclusion
Engaging in hobbies and special
interests
Interaction with members of voluntary
organizations
Engaging in occupational activities
Extraversion
Gregariousness
Informant ratings of how active
Friendliness
Gregariousness
Activity level
Social activity
Weekly social activity
.50
.30
.25
.22
.62
.30
.27
.48
.22
.20
.24
.19
.36
.51
.22
.21
.63
.31
.28
.50
.64
.33
.26
.24
.43
.21
.13b
.34
.36
.39
.35
Likeability and cooperation
Barsade et al., 2000
Barsade et al., 2000
Bell, 1978
62
20
120
112
112
186
186
186
186
186
186
186
186
186
Harker
Harker
Harker
Harker
114
114
114
114
&
&
&
&
Keltner,
Keltner,
Keltner,
Keltner,
2001a
2001a
2001a
2001a
MPQ well-being
MPQ well-being
Personal Feelings Scale
PANAS
PANAS
Delighted-Terrible Scale,
one-item happiness
one-item happiness
one-item happiness
one-item happiness
one-item happiness
one-item happiness
one-item happiness
one-item happiness
one-item happiness
FACS Duchenne smile
FACS Duchenne smile
FACS Duchenne smile
FACS Duchenne smile
⫺.30
.38
.43
.09
.06
.33
Fordyce
Task conflict
Group cooperativeness
Likeability as work partner
Intimacy of conversation
Degree of disclosure in conversation
Judged physical attractiveness
Fordyce
Judged intelligence/competence
.30
Fordyce
Judged social skills
.41
Fordyce
Judged public speaking ability
.28
Fordyce
Judged self-confidence
.36
Fordyce
Judged assertiveness
.25
Fordyce
Judged number of close friends
.35
Fordyce
Judged likelihood of having a strong
romantic relationship
Judged likelihood of having family
support
Observer-rated affiliation
Observer-rated negative emotionality
Judged positive emotionality
Judged competence
.33
Fordyce
.34
.69
⫺.57
.71
.21
(table continues)
812
Study
n
Effect size
(r)
Likeability and cooperation (continued)
King & Napa, 1998 (Study 1)
Mathes & Kahn, 1975 (female sample)
Mathes & Kahn, 1975 (male sample)
Perry et al., 1986 (eighth grade sample)
Rimland, 1982
Scheufele & Shah, 2000
Taylor et al., 2003
104
104
104
104
264
264
101
110
32
1,991
3,462
136
124
136
124
111
55
111
PANAS
PANAS
Three -item happiness
Happiness
Happiness
Dichotomous “Who is happier?‘
Dichotomous “Happy or not?‘
Four-item Index of Life Satisfaction
SWLS
SWLS
SWLS
SWLS
Three-measure composite
Ten-measure composite
PANAS
Partner-rated attraction
Partner-rated closeness
Judged moral goodness
Judged likelihood of going to heaven
Judged moral goodness
Judged likelihood of going to heaven
Helpfulness
Selfishness
Personality strength
Informant-rated warmth
Informant-rated friendliness
Informant-rated assertiveness
Informant-rated assertiveness
Judged managerial potential
Judged positive personal qualities
Interpersonal conflict
.34
.30
.29
.25
.26
.26
.37
.09
.44
⫺.60
.21
.28
.33
.20
.25
.20
.28
⫺.12
Prosocial behavior
Feingold, 1983 (male sample)
Feingold, 1983 (female sample)
George, 1991
George, 1991
Krueger et al., 2001a
87
88
221
221
397
144
124
260
260
Rigby & Slee, 1993
869
Strayer, 1980a
Williams & Shiaw, 1999
One-item happiness
One-item happiness
Job Affect Scale
Job Affect Scale
MPQ positive emotionality
PANAS
Four-item intensity of positive
experience
Four-item intensity of positive
experience
14
Observational count of happy affect
139
Watson 10-item positive affectivity
scale
Unselfishness
Unselfishness
Extrarole prosocial behavior
Customer service
Self-reported altruistic acts
Time spent helping
Time spent helping
Transpersonal commitment
.27
.09
.24
.26
.44
.36
.27
.21
Involvement in community service
.36
Tendency to act in a prosocial or
cooperative manner
Observational count of empathetic
responses
Anticipated organizational citizenship
behavior
.36
.59
.42
Physical well-being and coping
Achat et al., 2000a
Achat et al., 2000a
Audrain et al., 2001
Bardwell et al., 1999 (healthy sample)
Bardwell et al., 1999 (healthy sample)
Benyamini et al., 2000a
Carver et al., 1993a (presurgery assessment)
C. C. Chen et al., 1996
Dillon & Totten, 1989
Goldman et al., 1996
659
659
227
40
40
851
59
59
59
59
121
16
134
Irving et al., 1998
Kehn, 1995a
Keltner & Bonanno, 1997
Lox et al., 1999
Lutgendorf et al., 1999 (movers sample)
Lyons & Chamberlain, 1994
Lyons & Chamberlain, 1994
115
98
39
121
26
158
158
621
105
LOT
LOT
PANAS
One-item vigor
One-item vigor
12-item positive affect
LOT
LOT
LOT
LOT
General Health Questionnaire
Coping Humor Scale
Repair Subscale of the Trait
Meta-Mood Scale
Hope Scale
FACS Duchenne laughter
Affective Reactions Measure
Sense of Coherence Scale
Uplifts Scale
LOT
SHS
SHS
General health
Pain
Physical activity
Sleep quantity
Sleep quality
Self-reported health
Active coping
Coping by positive reframing
Coping by humor
Coping by denial
Engagement coping
Presence of upper respiratory infection
Reported illnesses
.23b
⫺.09b
.19
.32
.36
.49
.33
.41
.40
⫺.39
.31
⫺.58
⫺.21
Hope-related coping responses
Global health
Perceived adjustment
Amount of physical exercise
NK cell activity
Upper respiratory infection symptoms
Upper respiratory infection symptoms
Perception of life events
.35
.43
.31
.19
.49
⫺.03
⫺.23
.43
.41
813
Study
n
Effect size
(r)
Physical well-being and coping (continued)
McCrae & Costa, 1986 (Study 1)
McCrae & Costa, 1986 (Study 1)
Mishra, 1992a
Pettit et al., 2001a
Riddick, 1985 (male sample)
Riddick, 1985 (female sample)
Stone et al., 1987
Stone et al., 1994
Sullivan et al., 2001
Valdimarsdottir & Bovbjerg, 1997
(with daily NA)
Valdimarsdottir & Bovbjerg, 1997
(no daily NA)
Vitaliano et al., 1998a
Watson, 1988a
Watson, 1988a
Watson, 1988a
Watson, 2000
Watson, 2000
Weinglert & Rosen, 1995
Zinser et al., 1992
254
254
720
140
140
140
806
753
6,576
6,576
30
96
408
105
26
22
42
80
80
80
354
354
85
127
71
22
Bradburn’s Scales
Bradburn’s Scales
PANAS
PANAS
PANAS
SWB Index
SWB Index
Nowlis Mood Adjective Checklist
PANAS
MUNSH
PANAS
Profile of Mood States
Profile of Mood States
Uplifts-Hassles
10-item PA Scale (daily, over 6–8
weeks)
10-item PA Scale (daily, over 6–8
weeks)
Positive Emotionality Scale (daily)
Positive temperament
Positive temperament
PANAS (weekly, over 13 weeks)
PANAS (daily, over 6–7 weeks)
Positive mood checklist
Mood Adjective Check List
Coping effectiveness
Mature coping
Overall activity level
Presence and severity of medical
conditions
Cigarette use
Alcohol intake
Leisure activities
Leisure activities
Global health
Musculoskeletal pain
Secretory IgA antibody activity
Antibody activity
Global health
Self-reported physical health
NK cell activity
NK cell activity
NK cell activity
.27
.26
.61
⫺.26
⫺.24
⫺.22
.37
.44
.50
⫺.25
.44
.05
.19b
.23
0.64
.05
.26
Daily physical complaints
⫺.18
Daily physical exercise
Physical exercise
Injury visits to health center
Illness visits to health center
Somatic symptoms
Urges to smoke
.12
.12
.12
.15
.36
.39
⫺.10
⫺.38
Exploration strivings
Absorption in activities
Creative episodes
Creativity
Maximizing tendencies
.44
.33
.41
.25
⫺.21
⫺.34
⫺.17
⫺.10
⫺.28
⫺.17
Creativity and problem solving
Kashdan et al., 2004 (Study 2)
Kashdan et al., 2004 (Study 2)
Richards & Kinney, 1990
Schuldberg, 1990
Schwartz et al., 2002 (Sample 1)
214
214
48
334
82
72
100
401
752
220
Shapiro & Weisberg, 1999
52
83
Staw et al., 1994a
272
PANAS activated
PANAS activated
Diagnosis of manic periods
Hypomanic traits
SHS
SHS
SHS
SHS
SHS
SHS
General Behavior Inventory
(hypomanic plus biphasic)
Three-measure composite of
positive affectivity
Trait creativity
.33
Judged managerial performance
.20
Judged creativity
.30
Note. PA ⫽ positive; PANAS ⫽ Positive and Negative Affect Schedule; MPQ ⫽ Multidimensional Personality Questionnaire; SWLS ⫽ Satisfaction With
Life Scale; HM ⫽ Happiness Measure; MUNSH ⫽ Memorial University of Newfoundland Scale of Happiness; SHS ⫽ Subjective Happiness Scale; LOT ⫽
Life Orientation Test; MAACL ⫽ Multiple Adjective Affect Checklist; SWB ⫽ Subjective Well-Being; FACS ⫽ Facial Action Coding System; NEO ⫽
Neuroticism/Extraversion/Openness Scale; ER ⫽ emergency room.
Subscript a indicates that the study appears in more than one section or table. Subscript b indicates that the effect size was calculated controlling for one
or more other variables.
814
Table 2
Study Information and Effect Sizes for Seven Categories of Longitudinal Research
Study
n
Time period
Effect size
(r)
.35
.36
.27
.17
.18
.03b
.04b
⫺.02b
.03
⫺.36
.13
Work life
Diener et al., 2002
Graham et al., in pressa
Marks & Fleming, 1999
Pelled & Xin, 1999
Roberts et al., 2003
99
60
60
60
60
7,882
4,455
4,489
1,322
99
859
PANAS
On-item cheerfulness
One-item residual happiness
Nine-item SWB index
PANAS
MPQ communal positive
emotionality
MPQ agency positive emotionality
emotionality
emotionality
Attributional Style Questionnaire
Proportion of second interviews
Income
Income
Unemployment
Income
Absenteeism
Financial security
3 months
1 year
4 years
4.5 years
5 years
19 years
5 years
5 years
1–15 years
5 months
8 years
Financial security
Occupational attainment
8 years
8 years
.06
.19
Occupational attainment
Work autonomy
8 years
8 years
.16
.06
Work autonomy
Quarterly insurance commissions
.13
.27
Job autonomy, meaning, and variety
Gross annual salary
1.5 years
.24
Judged creativity
1.5 years
.16
44
8 years
6 months
to 1 year
1.5 years
3.5 years
.47
63
1 year
.46
859
859
859
859
Seligman & Schulman, 1986a
(Study 2)
Staw et al., 1994a
859
68
129
Staw et al., 1994a
191
Staw et al., 1994a
191
Wright & Staw, 1999a
(Study 1)
Wright & Staw, 1999a
(Study 2)
.23
Headey et al., 1991a
Lucas et al., 2003
Marks & Fleming, 1999a
Neyer & Asendorpf, 2001
Ruvolo, 1998a
(wives sample)
Ruvolo, 1998a
(wives sample)
Ruvolo, 1998a
(husbands sample)
Ruvolo, 1998a
(husbands sample)
Spanier & Furstenberg, 1982
Staw et al., 1994a
71
111
112
649
1,761
1,322
489
317
FACS Duchenne smile
FACS Duchenne smile
FACS Duchenne smile
One-item happiness
Nine-item SWB index
General Self-Esteem
One-item happiness
Marital satisfaction
Marital status
Single status
Marital status
Marital status
Closeness with all relationships
Marital well-being
31 years
6 years
22 years
6 years
4⫹ years
1–15 years
4 years
1 year
.20
.19
⫺.20
.30
.20
.09
.19b
.30
317
One-item happiness
1 year
.15
317
One-item happiness
Marital well-being
1 year
.28
317
One-item happiness
1 year
.40
180
251
Cantril’s Ladder Scale
Remarriage after divorce
Emotional and tangible support form
supervisors
2.5 years
1.5 years
.16
.25b
Mortality rate
Probability of dying relative to peers
Survival
CHD risk reduction
Age at death
Pain
Hospital visits
Emergency room visits
Health problems last 30 days
Lifetime
26–28 years
4 years
9 months
lifetime
2 days
1 day
1 day
5 years
Health
Danner et al., 2001
Deeg & van Zonneveld, 1989
Devins et al., 1990
Fitzgerald et al., 2000
Friedman et al., 1993
Gil et al., 2004a
Gil et al., 2004a
Gil et al., 2004a
180
2,645
97
42
1,178
3,565
3,546
3,546
4,455
No. of positive emotional words
One-item life satisfaction
Life Happiness Rating Scale
LOT
Cheerfulness-Humor
Daily Mood Scale
Daily Mood Scale
Daily Mood Scale
Two-item residual happiness
⫺.31
⫺.11
.15
.30b
⫺.09
⫺.06b
⫺.04b
⫺.06b
⫺.06b
815
Study
n
Time period
Effect size
(r)
Health (continued)
Kirkcaldy & Furnham, 2000
Koivumaa-Honkanen et al.,
2001
2002 (male sample)
2002 (female sample)
2004 (male sample)
2004 (female sample)
Krause et al., 1997
Four
databases
29,137
SWB
Automobile fatalities
8 years
⫺.56
Four-item life satisfaction
Suicides
⫺.03
14,348
14,789
11,037
11,099
330
Eight-item life satisfaction
Fatal intentional and unintentional
injuries
Fatal intentional and unintentional
injuries
Work disability pension for
psychiatric and nonpsychiatric
causes
Work disability pension for
psychiatric and nonpsychiatric
causes
Survival fatal and nonfatal coronary
heart disease
Up to 20
years
Up to 20
years
Up to 20
years
Up to 11
years
Kubzansky et al., 2001
1,306
1,306
1,306
⫺.06
⫺.02
⫺.11
Up to 11
years
⫺.12
11 years
.18
12 years
⫺.14
Fatal coronary heart disease
12 years
⫺.07
Nonfatal angina and heart attacks
12 years
⫺.12
Survival
Days survival
7 years
22.6 years
.36
.25
Mortality rate
Survival
Stroke incidence
3–6 years
2 years
6 years
⫺.06
.08
⫺.13b
Maier & Smith, 1999
Ostir et al., 2000
Ostir et al., 2001
(male sample)
Ostir et al., 2001
(female sample)
Palmore, 1969
513
2,276
772
Revised Optimism-Pessimism
Scale
Scale
Scale
Affect Balance Scale-Joy
Attitudes Toward Own Aging
Subscale
PANAS
CESD Positive Affect Scale
1,706
Stroke incidence
6 years
⫺.05b
Mortality rate
15 years
⫺.26
Peterson et al., 1998
1,097
One-item interviewer-rated
happiness
Optimistic (global) attributional
style
style
style
style
S
Life satisfaction scale
LOT
LOT
LOT
LOT
LOT
One-item vigor
Revised Dimension of
Temperament Survey
Temperament Survey
Temperament Survey
happiness
happiness
Mortality rate
Lifetime
⫺.11
Mental health problems
10 years
⫺.14
Poor adjustment
10 years
⫺.11
High levels of drinking
10 years
⫺.07
Survival
Survival
No. of days to begin walking
Physical recovery
Postsurgical quality of life
Resume vigorous exercise
Return to normal activities
Hockey injury
Delinquent activity
10 years
1 year
1 week
1 week
6 months
6 months
6 months
6 months
6 months
.13
.13
⫺.36
.35
.67
.33
.38
⫺.32
⫺.25
Delinquent activity
12 months
⫺.15
Delinquent activity
18 months
⫺.12
Mortality rate
2 years
⫺.07
Mortality rate
2 years
⫺.14
Levy et al., 1988
Levy et al., 2002 (Study 2)
36
660
265
(male sample)
(male sample)
(male sample)
Pitkala et al., 2004
Reynolds & Nelson, 1981
Scheier et al., 1989a
Smith et al., 1997
Windle, 2000a
491
154
46
46
45
45
44
86
1,016
Windle, 2000a
1,016
Windle, 2000a
1,016
Zuckerman et al., 1984
(healthy sample)
Zuckerman et al., 1984
(unhealthy sample)
622
622
622
182
168
104
100
FACS Duchenne smile
FACS Duchenne smile
Self-rated competence
Self-rated competence
22 years
31 years
.20
.29
(table continues)
816
Study
n
Time period
Effect size
(r)
Costa et al., 1981
Costa et al., 1981
396
114
234
104
100
408
Chicago Attitude Inventory
Chicago Attitude Inventory
Bradburn’s Scales
FACS Duchenne smile
FACS Duchenne smile
MUNSH
Extraversion
Extraversion
Extraversion
Activity level
2–10 years
10–17 years
10 years
22 years
31 years
1.5 years
.20
.24
.23
.23
.16
.29b
Judged creativity
1.5 years
.31b
Time spent engaged in volunteer
work
3 years
.04b
Creativity and prosocial behavior
Staw et al., 1994a
Thoits & Hewitt, 2001a
191
2,681
One-item happiness
Carver et al., 1993a
Cohen et al., 2003
Epping-Jordan et al., 1999
Epping-Jordan et al., 1999
Fredrickson & Joiner, 2002
Vitaliano et al., 1998a
791
678
525
59
59
59
59
334
80
80
138
1,683
1,252
1,33
47
47
408
23
LOT
LOT
LOT
LOT
Positive emotional style
LOT
LOT
PANAS
PANAS
LOT
LOT
MUNSH
Uplifts-Hassles
Global health
Global health
Global health
Active coping
Coping by positive reframing
Coping by humor
Coping by denial
Presence of clinical infection
Symptoms of anxiety/depression
Symptoms of anxiety/depression
Broad-minded coping
Alcohol intake
Smoking
Presence and severity of medical
conditions
Coping by information seeking
Coping by suppression
Global health
NK cell activity
1
3
5
3
3
3
3
1
3
6
5
5
5
5
year
years
years
months
months
months
months
month
months
months
weeks
years
years
weeks
.13b
.11b
.17b
.16
.26
.38
⫺.37
⫺.14
⫺.40
⫺.55
.19
⫺.04b
⫺.06b
⫺.31
1 week
1 week
1.5 years
1.5 years
.43
⫺.30
.28b
.48
Note. PA ⫽ positive affect; PANAS ⫽ Positive and Negative Affect Schedule; MPQ ⫽ Multidimensional Personality Questionnaire; FACS ⫽ Facial
Action Coding System; CES-D ⫽ Center for Epidemiologic Depression Scale; SWLS ⫽ Satisfaction With Life Scale; LOT ⫽ Life Orientation Test;
MUNSH ⫽ Memorial University of Newfoundland Scale of Happiness; HM ⫽ Happiness Measure; SWB ⫽ Subjective Well-Being; NK ⫽ natural killer.
Subcript a indicates that the study appears in more than one panel or table. Subscript b indicates that the effect size was calculated controlling for one or
more other variables
independence among them. To this end, several guidelines were
followed.
First, when more than one effect size was generated from the
same sample, and these effect sizes involved moderately to highly
correlated constructs (e.g., judgments of friendliness, gregariousness, and assertiveness), they were not treated independently. As a
result, the unweighted average of each set of correlated effect sizes
was used in our analyses. This procedure was used for effect sizes
generated from the same sample and reported within a particular
panel of a table (e.g., those involving work life in Table 1).
Second, as mentioned previously, we recognized that sometimes
the same sample was used to generate effect sizes in more than one
panel of a table (e.g., sociability/activity and health) or even across
tables (e.g., supervisory evaluations assessed at a single point in
time [in Table 1] as well as two points in time [in Table 2]). In such
instances, we selected only one specific effect size for our analyses. If the rs came from the same table, the effect size tapping the
best fitting construct was chosen. If the rs spanned Tables 1 and 2,
the effect size reflecting a longitudinal correlation was selected, as
longitudinal data speak relatively more strongly to causation.
Third, even when generated from the same sample or dataset,
effect sizes were considered independent if they involved variables
that are not highly correlated with one another (e.g., income and
alcohol consumption; see Meng, Rosenthal, & Rubin, 1992).
Defining Our Terms
What Is the Hallmark of Happiness?
Our focus in this article is on happy individuals—that is, those
who experience frequent positive emotions, such as joy, interest,
and pride, and infrequent (though not absent) negative emotions,
such as sadness, anxiety, and anger. Although many definitions of
happiness have been used in the literature, ranging from life
satisfaction and an appreciation of life to momentary feelings of
pleasure, we define happiness here as a shorthand way of referring
817
Table 3
Study Information and Effect Sizes for Six Categories of Experimental Research
Study
n
Affect induction
Comparison groups
Effect size
(r)
Dependent variable
Baron, 1987
71
False feedback
Positive vs. negative
and control
and control
Positive vs. control
Likeability of accomplice
.44
Baron, 1987
71
False feedback
Judgment of whether employee
matches job requirements
Self-efficacy
Self-set goals
Overall evaluation of accomplice
for job
Overall rating of employee
.43
Baron, 1990a
Baron, 1990a
Baron, 1993
80
80
92
Fragrance
Fragrance
Recall event(s)
Baron et al., 1992 (Study 1)
91
Lighting
Baron et al., 1992 (Study 1)
91
Lighting
Baron et al., 1992 (Study 3)a
80
Gift
High illuminance vs.
low illuminance
High illuminance vs.
low illuminance
Hiring decision
.34
Judgment of whether hypothetical
employee deserves merit raise
Judgment of whether hypothetical
employee deserves promotion
Perceived task performance
Certainty of future success
Attribution of successes to stable
causes
Recall of positive aspects of past
experiences
Interpersonal attraction
.23
80
Gift
Barsade, 2002a
Brown, 1984
Brown, 1984
93
61
61
Confederate role playing
Velten
Velten
.21
.73
.26
Clark & Waddell, 1983
45
False feedback
Griffitt, 1970
40
Temperature
Hom & Arbuckle, 1988
Samson & Rachman, 1989
Sarason et al., 1986 (Study 2)
31
84
60
Recall event(s)
Music and thought
Self-description task
Schuettler & Kiviniemi, in pressa
Wright & Mischel, 1982
Wright & Mischel, 1982
50
72
72
Velten
Recall event(s)
Recall event(s)
Extreme heat vs. normal
temperature
Happy vs. sad
Happy vs. sad
self-feelings
Goal setting
Self-efficacy
Positive self-references
.48
.28
.39
Self-efficacy regarding health
Satisfaction with performance
Recall of past successes
.43
.64
.75
Elated vs. neutral and
depressed
depressed
depressed
Interest in being with friends
.20
Interest in social activities
.31
Interest in leisure activities
.33
High self-disclosure
.44
Total communication
.53
and control
Positive vs. neutral
Amused vs. neutral
Interest in category-sorting task
.56
Attentive
Intrinsic interest
Perceived relationship closeness
.71
.74
.27
More monetary concessions
Resolving interpersonal conflict
through avoidance
through competition
Preference to resolve conflict
through avoidance
through avoidance
through collaboration
through collaboration
.23
⫺.23
.16
.22
.17
.53
.21
.36
.33
Cunningham, 1988a (Study 1)a
102
Velten
102
Velten
102
Velten
Cunningham, 1988b
77
Cunningham, 1988b
77
Hirt et al., 1996
McMillen et al., 1977 (Study 1)
Murray et al., 1990 (Study 3)
Waugh & Fredrickson, 2003
194
27
85
94
False feedback and
videotape
False feedback and
videotape
Velten
False feedback
Velten and film clip(s)
Film clip(s)
Negotiation and conflict resolution
Baron, 1990a
Baron, 1990a
80
80
Fragrance
Fragrance
Baron, 1990a
80
Fragrance
16
Humor
Humor vs. control
16
Flattery
Flattery vs. control
16
Flattery
Flattery vs. control
Baron et al., 1992 (Study 2)*
72
Lighting
Warm vs. cool lighting
⫺.23
⫺.50
⫺.50
.50
.29
(table continues)
818
Study
n
Affect induction
Comparison groups
Dependent variable
Effect size
(r)
Negotiation and conflict resolution (continued)
72
Lighting
Warm vs. cool lighting
Barsade, 2002a
Barsade, 2002a
Carnevale & Isen, 1986a
Forgas, 1998 (Study 1)
26
26
80
72
Cartoon(s) and gift
False feedback
Positive
Positive
Positive
Positive
72
False feedback
132
False feedback
132
False feedback
96
False feedback
96
False feedback
vs.
vs.
vs.
vs.
negative
negative
control
control
through avoidance
Judged group cooperativeness
Judged group conflict
Persist at negotiation
Planned use of cooperation as
bargaining strategy
Planned use of competition as
bargaining strategy
bargaining strategy
bargaining strategy
bargaining strategy
bargaining strategy
⫺.30
.44
⫺.42
.41
.32
⫺.32
.21
⫺.21
.30
⫺.24
Prosocial behavior
Aderman, 1972
Baron & Bronfen, 1994 (Study 2)
120
72
80
Velten
Fragrance
Lighting and gift
Berkowitz, 1987 (Study 1)
Berkowitz, 1987 (Study 2)
Carnevale & Isen, 1986a
108
60
80
102
Velten
Velten
Cartoon(s) and gift
Velten
90
160
80
78
14
12
10
20
20
52
Find dime
Find dime
Velten
Velten
False feedback
False feedback
False feedback
False feedback
False feedback
Gift
90
36
20
Recall event(s)
Recall event(s)
Stories
Cunningham et al., 1980 (Study
Isen, 1970 (Study 1)
Isen & Levin, 1972 (Study 1)
1)
2)
1)
2)
O’Malley & Andrews, 1983
Rosenhan et al., 1974
Rosenhan et al., 1981
Elated vs. depressed
Positive/high illuminance
vs. control/low
illuminance
depressed
Positive/help vs. control/
distraction
Happy vs. neutral
Volunteering
Time spent helping
Time willing to help
.45
.28
.25
Helping
Helping
Helping
Interest in prosocial activities
.34
.68
.62
.27
Helping
Donating to charity
Helping
Helping
Amount of donation
Helping
Attentiveness
Helping
Initiation of conversation
Willingness to help
.28
.21
.33
.34
.58
.57
.58
.58
.61
.36
Donating blood
Contributing to needy children
Helping experimenter
.26
.52
.59
Alden et al., 2001
Cogan et al., 1987 (Study 1)
Dillon et al., 1985
Fredrickson & Levenson, 1998
(Study 2)
Fredrickson et al., 2000 (Study 1,
Sample 1)
Fredrickson et al., 2000 (Study 1,
Sample 2)
Futterman et al., 1994
38
40
10
72
Mental imagery
Audiotape
Humorous videotape
Video clip(s)
Happy vs. control
Smiling vs. not smiling
95
Video clip(s)
Content vs. sad
75
Video clip(s)
Content vs. neutral
25
Reflect on scenario(s)
Lefcourt et al., 1990 (Study 1)
45
34
Humorous audio clip(s)
Humorous video clip(s)
Positive and negative
vs. control
Pre- vs. postaudio clip
Pre- vs. postvideo clip
McClelland & Cheriff, 1997 (Study 1)
McClelland & Cheriff, 1997 (Study 3)
41
57
85
50
50
Humorous Audio clip(s)
Video clip(s)
Video clip(s)
Velten
Velten
Pre- vs. postaudio clip
Pain ratings
Pain threshold
Immune function
Duration of cardiovascular
reactivity
reactivity
reactivity
Immune function
.51
.45
.38
⫺.26
⫺.27
⫺.28
.76
Immune function
Immune function
.47
.46
Immune function
Immune function
Immune function
Self-efficacy regarding health
Health perceptions
.50
.33
.05
.43
.53
819
Study
n
Affect induction
Comparison groups
Dependent variable
Effect size
(r)
Physical well-being and coping (continued)
50
Velten
Smith et al., 2004
82
Recall event(s)
Recall of close vs.
casual relationship
Treatment-related behavioral
intentions
Blood pressure reactivity to
stressful task
.31
⫺.23
Creativity and problem solving
Adaman & Blaney, 1995
Ambady & Gray, 2002 (Study 1)
Ambady & Gray, 2002 (Study 2)
Baron, 1990a
71
35
47
80
Music
Video clip(s)
Video clip(s)
Fragrance
Sad vs. neutral
Happy and control vs. sad
Happy and control vs. sad
Bless et al., 1996 (Study 1)
82
Write about event(s)
Happy vs. sad
61
Video clip(s)
Happy vs. sad
80
Video clip(s)
Happy vs. sad
Happy/accountable/no
stereotype activated
vs. neutral/not
accountable/stereotype
activated
Neutral vs. sad
Neutral vs. sad
Bodenhausen et al., 1994 (Study 4)
131
Recall event(s)
Dovidio et al., 1995
70
51
43
Recall event(s)
Recall event(s)
Candy
Elsbach & Barr, 1999
120
Erez & Isen, 2002 (Study 1)
Estrada et al., 1994
Forgas, 1989
97
97
97
44
29
29
72
Candy vs. difficult
anagram problems
Candy
Candy
Candy
Candy
Candy
Candy
False feedback
Forgas, 1989
72
False feedback
Isen et al., 1985 (Study 1)
29
Positive
Positive
Positive
Positive
Positive
Positive
Positive
vs.
vs.
vs.
vs.
vs.
vs.
vs.
neutral
neutral
neutral
control
control
control
negative
Positive/personal
relevance vs.
negative/impersonal
relevance
Isen & Daubman, 1984 (Study 1)
162
Word associations to
affectively valenced
words
Gift
162
Video clip(s)
74
Gift
Isen & Means, 1983
22
False feedback
Isen & Means, 1983
22
False feedback
Isen & Means, 1983
Kahn & Isen, 1993 (Study 1)
Kavanagh, 1987
Kavanagh, 1987
Mackie & Worth, 1989 (Study 1)
22
69
54
45
85
85
215
False feedback
Gift
Gift
Gift
Recall event(s)
Recall event(s)
False feedback
Mackie & Worth, 1989 (Study 2)
260
Video clip(s)
Positive/limited viewing
of Argument vs.
remaining conditions
Originality
Judgment accuracy
Judgment accuracy
Use of efficient strategy on coding
task
Reliance on preexisting general
knowledge structures
Avoiding stereotypic judgments
⫺.31
.83
.63
.22
Anchoring bias
Anchoring bias
Making more inclusive group
representations
Careful execution of steps in
highly structured task
Performance on anagram task
Motivation
Time spent on anagram task
Creativity
Time before diagnosis considered
Anchoring bias
Speed and efficiency in reaching
decision
Decision-making efficiency
⫺.24
⫺.27
.39
.26
.31
.31
.21
⫺.33
.21
.30
.33
.33
⫺.48
⫺.40
.32
.43
No. of unusual word associations
.48
Tendency to group items as
belonging together
belonging together
belonging together
Propensity to return to alreadyreviewed information
Pieces of information considered
during decision making
Time to complete mental task
Variety seeking
Variety seeking
Variety seeking
Performance on anagram task
Time spent on anagram task
Differentiation of strong vs. weak
arguments
.23
.29
.40
⫺.51
⫺.26
⫺.43
.31
.31
.23
.32
.34
⫺.18
Differentiation of strong vs. weak
⫺.15
arguments
(table continues)
820
Study
n
Affect induction
Comparison groups
Dependent variable
Effect size
(r)
Creativitity and problem solving (continued)
Melton, 1995
Reed & Aspinwall, 1998
57
66
Cartoon(s)
Survey
Humor vs. control
Reed & Aspinwall, 1998
66
Survey
Sinclair & Mark, 1995 (Study 1)
39
Velten
Sinclair & Mark, 1995 (Study 2)
83
Velten
and neutral
Trope & Pomerantz, 1998 (Study 3)
68
False feedback
Urada & Miller, 2000 (Study 1)
43
Recall event(s)
Urada & Miller, 2000 (Study 4)
61
Recall event(s), music,
candy
Performance on syllogisms
No. of facts ignored about caffeine
and fibrocystic breast disease
Recall of disconfirming
information about caffeine and
fibrocystic breast disease
Accuracy of correlational
judgments
Accuracy of correlational
judgments
Interest in receiving feedback
regarding liabilities vs. assets
Using broad definition of group
membership
Using broad definition of group
membership
⫺.43
⫺.22
.28
.43
.25
.35
.30
.25
Note. Subscript a indicates that the study appears in more than one section or table.
to the frequent experience of positive emotions. In our theoretical
framework, it is the experience of positive emotions that leads to
the behavioral outcomes we review, and “happiness” describes
people who experience such emotions a large percentage of the
time (Diener, Sandvik, & Pavot, 1991). Although more inclusive
definitions of happiness have been offered by others (e.g., Veenhoven, 1984), we restrict our definition to the experience of
frequent positive affect because that definition encompasses the
findings we review.
The notion that frequent positive affect is the hallmark of
happiness has strong empirical support. Diener and his colleagues (1991) found that the relative proportion of time that
people felt positive relative to negative emotions was a good
predictor of self-reports of happiness, whereas the intensity of
emotions was a weaker predictor. That is, happy people feel
mild or moderate positive affect the majority of the time; they
do not appear to experience frequent intense positive states. In
several studies and using a variety of happiness measures,
Diener and his colleagues found that happy people experienced
positive moods and emotions most of the time (see also Diener,
Larsen, Levine, & Emmons, 1985). Indeed, people who report
high levels of happiness appear to have predominantly positive
affect—that is, stronger positive feelings than negative ones—
80% or more of the time. For example, in a large international
sample of more than 7,000 college students in 41 diverse
nations collected by Diener’s laboratory, individuals who reported that they were pleased with their lives expressed feelings
of joy over half of the time.
In the World Value Survey I, which comprises probability
samples of almost 60,000 adults in 41 nations, 64% of the respondents reported more positive than negative affect, with only 18%
reporting more negative than positive affect (World Value Survey
Group, 1994). Notably, of those reporting above-neutral happiness
on this survey, nearly everyone reported more positive than negative affect, prompting Diener et al. (1991) to conclude that
happiness is best regarded as a state in which people feel a
preponderance of positive emotions most of the time. One reason
for the tendency of happy people to feel positive emotions more
frequently may be that the chronically happy are relatively more
sensitive to rewards in their environment—that is, they have a
more reactive behavioral approach system (Gray, 1994)—and are
more likely to approach, rather than avoid, rewarding situations
(Watson, 1988). In addition, Larsen and colleagues demonstrated
that dispositional positive affectivity involves a susceptibility to
experience positive moods (Larsen & Ketelaar, 1991; Rusting &
Larsen, 1997).
Characterizing and Measuring High Average Positive
Affect
In short, the research evidence supports the notion that it is the
amount of time that people experience positive affect that defines
happiness, not necessarily the intensity of that affect. Furthermore,
happy people have been found to experience positive emotions the
majority of the time. Thus, in this article, we identify happy individuals as those who experience high average levels of positive affect.
These high average levels of positive affect, which we variously refer
to as chronic happiness, trait PA, or subjective well-being, may be
rooted in personality predispositions (e.g., a genetically determined
“set point”; Lyubomirsky, Sheldon, & Schkade, 2005), the person’s
current life circumstances, the person’s intentional activities, or all of
these. Because the existing literature does not discriminate which
effects of long-term PA come from which of these different sources,
we refer in our article to individuals who show high average level of
PA, without reference to the source of this state. However, the
findings from the experimental studies suggest that positive emotions
can produce desirable outcomes even in the absence of a very happy
disposition, although a happy disposition is likely to be a cause of
positive emotions.
The research we cite uses a variety of measures of long-term
PA, happiness, and well-being. The vast majority of assessment of
chronic PA is by self-report measures, which have been validated
821
Table 4
Measures of Central Tendency and Dispersion for Effect Sizes by Category
Mean of effect sizes
Category
n
Sampling
units
Unweighted
Weighted
Median of effect sizes
Unweighted
Weighted
␭2 test of
heterogeneity
Cross-sectional data
Work life
Health
Positive
perceptions of
self and others
Sociability and
activity
Likeability and
cooperation
Prosocial behavior
Physical well-being
and coping
Creativity and
problem solving
19
22
19
16
34,794
120,256
17,693
2,821
.27
.27
.32
.39
.20
.15
.32
.42
.29
.23
.31
.37
.20
.07
.31
.39
188.82****
3,079.60****
67.98****
59.36****
26
11,773
.33
.37
.32
.40
116.20****
15
6,930
.32
.34
.29
.21
217.64****
7
25
2,097
5,093
.32
.29
.35
.31
.32
.31
.36
.35
11.45
197.32****
10
2,275
.26
.24
.27
.28
18.25*
Longitudinal data
Work life
Health
Positive
perceptions of
self and others
Sociability and
activity
Creativity and
prosocial
behavior
Physical well-being
and coping
11
8
26
1
15,080
5,106
37,421
100
.24
.21
.18
.25
.05
.19
.09
N/A
.25
.20
.14
.25
.03
.20
.09
N/A
621.63****
30.43****
418.90****
N/A
4
1,117
.25
.29
.24
.33
2
2,872
.18
.06
.18
.04
55.67****
10
2,999
.27
.15
.29
.14
80.98****
4.66
Experimental data
Positive
perceptions of
self and others
Sociability and
activity
Negotiation and
conflict
resolution
Prosocial behavior
Physical well-being
and coping
Creativity and
problem solving
* p ⬍ .05.
** p ⬍ .01.
13
900
.36
.34
.36
.33
19.66
6
579
.51
.49
.52
.56
16.30**
8
574
.33
.29
.31
.27
4.15
17
14
1,170
749
.43
.38
.37
.34
.36
.40
.34
.28
26.53*
18.79
34
2,707
.25
.16
.30
.25
*** p ⬍ .001.
193.63****
**** p ⬍ .0001.
in a number of studies (e.g., Sandvik, Diener, & Seidlitz, 1993;
Watson, 2000). In experimental research on induced moods, positive moods are operationalized by the manipulations that induce
them; in longitudinal research, the measures depend on moods at
the moment or over a recent period such as the past week or
month; and, in individual difference research on chronic positive
emotions, the measures usually ask about the person’s moods in
general. Although these methods are not without shortcomings,
they appear to be at least moderately valid. A more detailed
discussion of measurement issues is available elsewhere (see Diener, 1994; Diener et al., 1999; Sandvik et al., 1993).
Discriminating Happiness and Positive Affect From
Related Constructs
Notably, a large number of studies have assessed life satisfaction as an indicator of well-being, and such investigations are
occasionally included here. Although the construct of satisfaction
822
is not identical to positive affect—some researchers argue that it
has an affective dimension (Veenhoven, 1997), whereas others
define it as a purely cognitive judgment of life or its facets (Diener
et al., 1999)—we review studies of satisfaction because they
frequently represent the only available evidence in an area. Furthermore, life satisfaction and positive affect have been found to
correlate at around .40 to .50 in undergraduates (Lucas, Diener, &
Suh, 1996) and .52 in business students (Staw & Barsade, 1993).
In addition, of people who say they are above neutral in satisfaction with their lives, 85% have been found to report that they feel
happy at least half of the time (Lucas et al., 1996). Thus, life
satisfaction is a defensible proxy for chronic happiness, in cases in
which no studies exist using more direct measures of happiness;
Lucas and his colleagues demonstrated that it is separable but not
independent from chronic PA. Similarly, Lucas et al. found that
optimism is also related to positive affectivity, but separable from
it. Again, we sometimes refer to findings based on measures of
optimism because the findings can be striking, but we eagerly
await the day when a full set of findings based on measures of
positive affect, as well as related concepts, is available.
The sections of this article that address research on positive
affect similarly include studies using a variety of affect measures
and mood inductions. Most researchers focus on global pleasant
affect, without discriminating among specific positive emotions or
between emotions and moods, and our review reflects this characteristic of the field. Finally, when no research on positive affect
is available, we infrequently cite the literature on negative affect or
depression. Although positive affect and negative affect often
exhibit a degree of independence in the long run (e.g., Diener,
Smith, & Fujita, 1995), these two types of affect regularly show
moderate inverse relations across individuals, justifying the use of
such negative states as the inverse of PA or subjective well-being,
to address our questions when more direct measures are absent.
Furthermore, depression has been defined not only by high levels
of negative affect, but also low levels of PA (Watson & Clark,
1995).
CROSS-SECTIONAL EVIDENCE
Question 1: Are Happy People Successful People?
Being successful means accomplishing those things that are
valued by one’s culture, flourishing in terms of the goals set forth
by one’s society. Hence, our focal question is whether happy
people on average are better able to achieve the values and goals
they have been socialized to believe are worthwhile. As Sigmund
Freud reportedly once said, lieben und arbeiten—to love and to
work—are what a “normal” person should be able to perform well.
Few people would oppose, in any culture, the addition of health to
love and work as a critical ingredient to a successful life.
Accordingly, the following section is divided into three parts:
work life, social relationships, and health. Specifically, in this
section, we review the cross-sectional evidence addressing the
question of whether happy people are relatively more successful in
various life domains, ranging from marriage to work, from altruistic community involvement to making money, and from mental
health to physical health and longevity.
Work Life
In modern Western society, work fills a large number of people’s waking hours. Furthermore, it is important in terms of producing income, influencing self-esteem, creating opportunities for
meaningful activities, and producing the goods and services
needed by society. Thus, work is highly valued. Are happy people
more successful than their less happy peers on job-related and
performance variables? We will first review the correlational evidence bearing on this question (for study information and effect
sizes, see Panel 1 of Table 1).
Employment and Quality of Work
The cross-sectional evidence reveals that happy workers enjoy
multiple advantages over their less happy peers. Individuals high
in subjective well-being are more likely to secure job interviews,
to be evaluated more positively by supervisors once they obtain a
job, to show superior performance and productivity, and to handle
managerial jobs better. They are also less likely to show counterproductive workplace behavior and job burnout.
Even before entering the workforce, people with high subjective
well-being are more likely to graduate from college (Frisch et al.,
2004). Furthermore, happy individuals appear to secure “better”
jobs. In one study, employees high in dispositional positive affect
had jobs, as rated by trained observers, that had more autonomy,
meaning, and variety (Staw, Sutton, & Pelled, 1994). Finally,
evidence from a variety of sources shows that happy people are
more satisfied with their jobs (e.g., Connolly & Viswesvaran,
2000; Tait, Padgett, & Baldwin, 1989; Weiss, Nicholas, & Daus,
1999). In a meta-analysis of 27 studies of affect and job satisfaction, Connolly and Viswesvaran concluded that 10%–25% of the
variance in job satisfaction was accounted for by measures of
dispositional affect. In their analyses, the mean corrected correlation between positive affect and job satisfaction was .49.
Once a happy person obtains a job, he or she is more likely to
succeed. Employees high in dispositional positive affect receive
relatively more favorable evaluations from supervisors and others
(Staw et al., 1994). For example, in Staw and colleagues’ study,
managers of high positive affect employees of three Midwestern
organizations gave them higher evaluations for work quality, productivity, dependability, and creativity. Wright and his colleagues
have replicated this effect, showing that happy people receive
higher ratings from supervisors (Cropanzano & Wright, 1999;
Wright & Staw, 1999). Finally, work performance may be more
strongly predicted by well-being than by job satisfaction. In two
studies, Wright and Cropanzano (2000) found that job performance, as judged by supervisors, was significantly correlated with
well-being (rs of .32 and .34, respectively), but uncorrelated with
measures of job satisfaction (rs of ⫺.08 and .08, respectively).
Staw and Barsade (1993) found that, as rated by objective
observers, those high in dispositional positive affect performed
objectively better on a manager assessment task (including leadership and mastery of information). Other evidence for happy
people’s relative success on the job includes findings that individuals high in dispositional positive affect are more likely to be in the
supervisory in-group (Graen, 1976). Dormitory resident advisors
were rated by residents as being more effective if they were high
on trait positive affect (DeLuga & Mason, 2000), and happier
cricket players had higher batting averages (Totterdell, 2000).
George (1995) found that service departments with happy leaders
were more likely to receive high ratings from customers, and that
the positive affective tone of the sales force was an independent
predictor of customer satisfaction. Corroborating these results, a
recent study showed that CEOs of manufacturing companies with
high positive affect were relatively more likely to have employees
who rated themselves as happy and healthy, and who reported a
positive, warm climate for performance. In turn, organizational
climate was correlated with productivity (r ⫽ .31) and profitability
(r ⫽ .36; Foster, Hebl, West, & Dawson, 2004). Of interest, these
patterns were not found for negative affect. Finally, optimistic life
insurance agents appear to sell more insurance (Seligman & Schulman, 1986), and optimistic CEOs receive higher performance
ratings from the chairpersons of their boards and head companies
with greater returns on investment (Pritzker, 2002). Coté (1999)
reviewed the effects of well-being on job performance, and concluded that the causal relation between pleasant affect and strong
performance is bidirectional.
Undoubtedly, one of the reasons that happy, satisfied workers
are more likely to be high performers on the job is that they are less
likely to show “job withdrawal”—namely, absenteeism, turnover,
job burnout, and retaliatory behaviors (Donovan, 2000; Locke,
1975; Porter & Steers, 1973; Thoresen, Kaplan, Barsky, Warren, &
de Chermont, 2003). For example, positive moods at work predicted lower withdrawal and organizational retaliation and higher
organizational citizenship behavior (Donovan, 2000; see also
Credé, Chernyshenko, Stark, & Dalal, 2005; Miles, Borman, Spector, & Fox, 2002; Thoresen et al., 2003), as well as lower job
burnout (Wright & Cropanzano, 1998). Positive affect at work has
also been found to be directly associated with reduced absenteeism
(George, 1989). Finally, those who experience low arousal positive
affect on the job are less likely to want to quit and to be in conflict
with other workers (Van Katwyk, Fox, Spector, & Kelloway,
2000).
823
comes from studies of individuals who are satisfied with their jobs.
Notably, both positive affect on the job and chronic happiness have
been found to predict job satisfaction (Weiss et al., 1999). In turn,
job satisfaction predicts organizational citizenship behavior—that
is, acts that go beyond the requirements of the job, such as
spreading goodwill and aiding coworkers (Donovan, 2000; George
& Brief, 1992; Organ, 1988). However, studies of recurring positive affect corroborate these results. Borman, Penner, Allen, and
Motowildo (2001) reviewed evidence showing that positive affect
predicts organizational citizenship, and that negative affect inversely correlates with it, even when peer ratings rather than
self-ratings of citizenship are used. In addition, George and Brief
argued that habitual positive affect at work is pivotal in understanding so-called “organizational spontaneity,” which includes
helping coworkers, protecting the organization, making constructive suggestions, and developing one’s own abilities within the
organization (see also Donovan, 2000).
Community Involvement
Despite a scarcity of studies in this area, some evidence underscores the contributions of happy people to their communities.
Happy people appear to volunteer at higher levels than their
unhappy peers for charity and community service groups, including religious, political, educational, and health-related organizations (Krueger, Hicks, & McGue, 2001; Thoits & Hewitt, 2001)
and to invest more hours in volunteer service (Thoits & Hewitt,
2001). Furthermore, in a study of Israeli high school students,
those with high positive affect were more likely to be involved in
community service and to express a desire to contribute to society
and be of assistance to others (Magen & Aharoni, 1991). In
summary, as we describe in the section on prosocial behavior,
happy people seem to be relatively more inclined to help others
(Feingold, 1983).
Social Relationships
Income
An important indicator of success in modern societies is income.
Do happier people earn higher incomes? Several studies suggest
the answer to be yes. For example, a study of 24,000 German
residents revealed a correlation between income and life satisfaction of .20 (Lucas, Clark, Georgellis, & Diener, 2004), and a study
of Russians revealed correlations between real household income
and happiness of .48 in 1995 and .39 in 2000 (Graham, Eggers &
Sukhtankar, in press). Among indigenous Malaysian farmers, the
correlation between life satisfaction and material wealth (their only
available indicator of income) was found to be .23 (Howell,
Howell, & Schwabe, in press). According to Diener and BiswasDiener (2002), most surveys report correlations between income
and happiness in the range of .13–.24. In a meta-analysis of 286
empirical investigations of older adults, income was significantly
correlated with happiness and life satisfaction, and, surprisingly,
more so than with education (Pinquart & Sörensen, 2000).
Organizational Citizenship
Are happy workers relatively better organizational “citizens”?
Much of the cross-sectional evidence pertaining to this question
Berscheid (2003) highlighted the centrality of social relationships to successful human functioning when she wrote that “relationships constitute the single most important factor responsible
for the survival of homo sapiens” (p. 39). Do happy people have
better social relationships than their less happy peers? Our review
reveals this to be one of the most robust findings in the literature
on well-being. Next, we begin by presenting cross-sectional evidence regarding the question whether individuals high in trait
positive affect, happiness, and life satisfaction have more friends
and more social support, as well as experience happier interpersonal relationships. Relevant study information is contained in
Panel 2 of Table 1.
Friendship and Social Support
Do happy people have more friends and stronger social support
networks than less happy people? Cross-sectional studies have
documented an association between chronic happiness and the
actual number of friends or companions people report they can rely
on (Baldassare, Rosenfield, & Rook, 1984; Lee & Ishii-Kuntz,
1987; Mishra, 1992; Phillips, 1967; Requena, 1995), as well as
overall social support and perceived companionship (Baldassare et
824
al., 1984; see Pinquart & Sörensen, 2000, for a large metaanalysis). In the workplace, employees with high dispositional
positive affect have been found to receive more emotional and
tangible assistance from both coworkers and supervisors (Staw et
al., 1994).
Friendship has been found to have one of the highest positive
correlations with self-rated happiness (Campbell, Converse, &
Rogers, 1976). For example, the happiest college students (the top
10%) have been shown to have high-quality social relationships
(Diener & Seligman, 2002). In a meta-analysis of 286 studies, the
quantity and quality of contacts with friends was a strong predictor
of well-being, even stronger than that of contacts with family
members (Pinquart & Sörensen, 2000). Happy people also report
being more satisfied with their friends and their social activities
(Cooper, Okamura, & Gurka, 1992; Gladow & Ray, 1986; Lyubomirsky, Tkach, & DiMatteo, in press) and less jealous of others
(Pfeiffer & Wong, 1989). Not surprisingly, loneliness is negatively
correlated with happiness, especially in older adults (Lee & IshiiKuntz, 1987), and positively correlated with depression (Peplau &
Perlman, 1982; Seligman, 1991).
Marriage and Romance
More than 9 in 10 people worldwide eventually get married
(Myers, 2000), and an even greater percentage are in committed
intimate relationships. Surveys show that married people are happier than those who are single, divorced, or widowed (Diener et al.,
1999). For example, in a study of 19 countries, Mastekaasa (1994)
found that married people were happier than all of the other
groups. Numerous studies with respondents from diverse cultures
support this finding (e.g., Diener, Gohm, Suh, & Oishi, 2000;
Glenn & Weaver, 1979; Graham et al., in press; Kozma & Stones,
1983; Lee, Seccombe, & Shehan, 1991; Marks & Fleming, 1999;
Stack & Eshleman, 1998). Happy individuals tend to have fulfilling marriages and to be more satisfied with their marriages. Indeed, several writers have suggested that satisfaction with marriage and family life is the strongest correlate of happiness
(Headey, Veenhoven, & Wearing, 1991; Myers, 1992, 2000). For
example, data from six U.S. national surveys indicate that marital
happiness is more strongly related to global, personal happiness
than any other kind of domain satisfaction (Glenn & Weaver,
1981). Individual happiness is even associated with high marital
satisfaction in one’s spouse (Ruvolo, 1998).
The findings on marriage generalize to other romantic relationships. Undergraduates high in trait positive affect are more likely
than those low in trait positive affect to describe their current
romantic relationship as being of higher quality (Berry & Willingham, 1997), and happy people who are either married or in
committed relationships are more likely to describe their partner as
being their “great love” than their less happy peers (Willi, 1997).
Health
“A merry heart doeth good like a medicine: but a broken spirit drieth
the bones.”
—Proverbs 17:22
Are happier people healthier in general? In this section, we
review the cross-sectional evidence addressing the question
whether happiness is associated with superior mental and physical
health (see Table 1, Panel 3). Because only longitudinal studies
can address whether happy individuals have higher odds of survival and longevity, these studies are reviewed in the section
discussing the longitudinal literature. For a more detailed review,
focusing entirely on the relations between positive affect and
physical health, see Pressman and Cohen (2005).
Mental Health
Because positive affective experience has been described as an
important component of mental health (e.g., Jahoda, 1958; Taylor
& Brown, 1988), it would not be surprising to find that happy
individuals are more mentally healthy than their less happy peers.
Diener and Seligman (2002) reported that their happiest group of
people had few symptoms of psychopathology, such as depression,
hypochondriasis, or schizophrenia (see also Chang & Farrehi,
2001; Lu & Shih, 1997; Phillips, 1967). As the absence of positive
affect has been argued to be a distinguishing characteristic of
depression (L. A. Clark, Watson, & Mineka, 1994; Watson &
Clark, 1995), individuals high in trait positive affect are, of course,
less likely to suffer from this debilitating condition (e.g., Lyubomirsky et al., 2005), as well as from social phobia or anxiety
(Kashdan & Roberts, 2004). Parallel findings are observed when a
construct closely related to happiness— optimism—is examined.
Dispositional optimism has been shown, for example, to relate to
higher levels of self-reported vitality and mental health (Achat,
Kawachi, Spiro, DeMolles, & Sparrow, 2000) and lower levels of
depression (e.g., Chang & Farrehi, 2001).
Substance abuse is another indicator of poor mental health.
Although positive affect is clearly an initial outcome of engaging
in some potentially detrimental behaviors (i.e., individuals may
smoke or consume drugs to feel good), evidence suggests that
positive mood is related to a lower probability of drug use. Thus,
not surprisingly, happy individuals are less likely to report a
history of substance abuse (Bogner, Corrigan, Mysiw, Clinchot, &
Fugate, 2001). Furthermore, lowered positive affect is associated
with delinquent activity in adolescents (Windle, 2000).
Physical Health
According to the cross-sectional data, do happy people show
superior physical health? Unfortunately, direct, non–self-report
evidence is limited. Not surprisingly, happy people self-report
better health and fewer unpleasant physical symptoms (Kehn,
1995; Lyubomirsky et al., in press; Mroczek & Spiro, 2005;
Røysamb, Tambs, Reichborn-Kjennerud, Neale, & Harris, 2003).
Positive affect has been shown to relate to quality of life in cancer
patients over the course of their illnesses (Collins, Hanson, Mulhern, & Padberg, 1992) and to smaller allergic reactions among
healthy students (Laidlaw, Booth, & Large, 1996). In a study of
individuals with sickle cell disease, positive mood was associated
with fewer emergency room and hospital visits, fewer calls to the
doctor, less medication use, and fewer work absences (Gil et al.,
2004). In addition, those patients with positive moods were relatively less likely to report pain on the same day and 2 days later
(Gil et al., 2004). The number of days of work missed because of
health problems was also related to happiness in a large Russian
study (Graham et al., in press).
Finally, studies using variables correlated with subjective wellbeing show similar results. Optimism predicted less pain in a
sample of aging veterans (Achat et al., 2000), even after controlling for such variables as age, body mass index, and chronic health
conditions; optimistic women were less likely to deliver low-birth
weight infants (Lobel, DeVincent, Kaminer, & Meyer, 2000).
2.
sociability and activity,
3.
likability and cooperation,
4.
prosocial behavior,
5.
physical well-being and coping, and
Summary
6.
problem solving and creativity.
In summary, our review of the cross-sectional empirical literature suggests that happiness is positively correlated with indicators
of superior mental and physical health. Happiness, as well as the
concomitant experience of frequent positive affect, likely plays a
role in health through its effects on social relationships, healthy
behavior, stress, accident and suicide rates, and coping, as well as
possible effects on immune function. These variables are discussed
in the next section.
Conclusion
Are happy people better off? Although the research in some
areas is limited, our examination of the cross-sectional correlational literature thus far suggests that high subjective well-being is
related to positive outcomes in many areas of life. Specifically, as
displayed in the first three panels of Table 1 and on the top of
Table 4, happy people appear to be more successful than their less
happy peers in the three primary life domains: work (mean r ⫽
.27),1 relationships (mean r ⫽ .27), and health (mean r ⫽ .32).
Question 2: Are Long-Term Happiness and Short-Term
Positive Affect Associated With Behaviors Paralleling
Success?
The effect sizes presented in Tables 1 and 4 indicate robust
associations between happiness and desirable life outcomes. Is this
because successes bolster happiness, or the reverse? In this section,
we pull together evidence consistent with the argument that it is
happiness that promotes success, in part because happy individuals
show numerous adaptive characteristics. That is, we turn to examining whether happy people are relatively more likely to exhibit
behaviors and thoughts that parallel culturally valued success and
thriving—that is, the attributes, resources, and skills that help
people thrive and succeed.
Earlier, we reviewed evidence showing that the key indicator of
happiness is the experience of frequent positive emotions. Hence,
it is also important to investigate whether positive emotions and
moods are associated with desirable characteristics. We anticipate
that the correlations involving long-term happiness will be parallel
to those of short-term positive moods.
In summary, when the relevant data are available, we document
the characteristics that cooccur with happiness and PA. In conducting this review of the literature, we attempted to find as many
empirical investigations as possible that included measures of
happiness and positive affect and at least one other valenced
construct. The resulting collection of articles yielded six categories
of studies reporting significant correlates of long-term happiness
and short-term PA:
Each category is discussed in detail in the following sections,
and relevant study information is presented in the last six panels
(Panels 4 –9) of Table 1. Effect sizes appear in Tables 1 and 4.
Positive Perceptions of Self and Others
Self-Perceptions
Are self-nominated chronically happy people inclined to evaluate themselves and their futures in positive ways? It appears that
happy people are characterized by high personal competence and
self-esteem (Campbell et al., 1976; Kozma & Stones, 1978; Lyubomirsky et al., 2005; Scheufele & Shah, 2000; Schimmack,
Oishi, Furr, & Funder, 2004; Tarlow & Haaga, 1996), optimism
(Campbell, 1981; Lyubomirsky et al., in press), and a sense of
personal mastery and control (Csikszentmihalyi & Wong, 1991;
Grob, Stetsenko, Sabatier, Botcheva, & Macek, 1999; Lyubomirsky et al., in press; Ryff, 1989). For example, Lucas and colleagues
(1996) found that life satisfaction was consistently related to
self-esteem and optimism in multimethod assessments in which
one characteristic was measured by self-report and the other was
reported by informants. Happiness is also related to positive perceptions of all life domains. Happy and contented individuals have
been found to be satisfied with their family life, their romantic
relationships and their friends, their health, their education and
their jobs, their leisure activities, and even their housing and
transportation (e.g., Lyubomirsky et al., in press; Weiss et al.,
1999). Are the correlational findings regarding short-term positive
affect parallel to those for long-term happiness? The few correlational studies in this area examining happy moods suggest that PA
is also associated with relatively more positive self-perceptions.
For example, in a correlational study, higher levels of positive
affect were related to being less self-critical (Mongrain & Zuroff,
1995). Furthermore, participants in a naturally occurring good
mood set higher goals for themselves and reported more selfefficacy on a laboratory clerical task (Jundt & Hinsz, 2001), and
cricket players judged their performances more favorably (Totterdell, 2000).
Perceptions, Memories, and Judgments of Others
Chronically happy individuals have also been shown to have a
more positive attitude toward others. For example, in one study,
participants interacted with a female confederate in the laboratory,
then viewed a series of videotapes depicting an unfamiliar student
in various situations (Lyubomirsky & Tucker, 1998). Relative to
their unhappy peers, happy participants recalled the person they
1
1.
positive perceptions of self and others,
825
Mean rs cited within the text are all unweighted by sample size, as our
goal was to generalize across studies, not across individuals.
826
met in more favorable terms—for example, as kind, self-assured,
open, tolerant, and warm—and reported wanting to be friends with
her and to be her partner in a class project. Happy participants also
liked the person they saw on videotape more than did unhappy
ones. Berry and Hansen (1996) found that when participants were
“accidentally” left alone with a fellow student, those high in trait
PA liked their partner more than those low in trait PA. In another
study, happy faculty were found to write relatively more favorable
letters of recommendation, and happy undergraduates wrote relatively more positive recommendations for hypothetical employees
(Judge & Higgins, 1998). Finally, college women who were generally happy were less likely to be hostile toward other women
than women who were less happy (Cowan, Neighbors, DeLaMoreaux, & Behnke, 1998).
Happy people also feel more positive than their unhappy peers
toward the people that they know. They judge their friends,
spouses, and families more favorably (Cooper et al., 1992; Gladow
& Ray, 1986; Glenn & Weaver, 1981; Lyubomirsky et al., in
press), and are less jealous of other people competing for their
partner’s affections (Pfeiffer & Wong, 1989). However, we found
only one correlational study examining whether short-term PA is
also associated with greater liking and fondness for others. In this
investigation, happy moods were found to be associated with
positively toned inferences and attributions (Mayer, Mamberg, &
Volanth, 1988). For example, after reading a fictitious biography
of “Jim,” students who had relatively higher positive affect were
more likely to agree with positive inferences from the narrative
(e.g., “Jim is interesting”) and less likely to agree with negative
inferences (e.g., “Jim will get divorced”; Mayer et al., 1988).
Sociability and Activity
Do happy people tend to be social and active people? In this
section, we review the cross-sectional literature examining
whether chronically happy people are relatively more likely to
participate in a variety of social and physical activities, to enjoy
their leisure time, and to experience more energy and “flow”
(Csikszentmihalyi, 1999).
Sociability and Extraversion
The literature on the relations of happiness with sociability and
extraversion suggests that happy individuals have outgoing, extraverted personalities. Extraverts are warm, gregarious, sociable,
assertive, interested in new things, affiliative, lively, active, and
energetic (see Lucas, 2001, for a review). Extraversion has been
found to be related to happiness, chronic PA, and life satisfaction
in many investigations—among both undergraduates and old people, in questionnaire studies and in diary studies, and across many
nations (e.g., Brebner, Donaldson, Kirby, & Ward, 1995; Diener &
Seligman, 2002; Lucas, Diener, Grob, Suh, & Shao, 2000; Lyubomirsky et al., in press). Indeed, the findings of a meta-analysis
revealed that, of the most widely researched personality traits, the
highest average correlation with happiness was affiliation, the
inclination to relate to other people (DeNeve & Cooper, 1998). It
is noteworthy that Lucas and colleagues (2000) showed that positive affectivity is the “glue” holding together various aspects of
extraversion such as ascendance, sociability, and affiliation.
Cross-sectional studies have shown that levels of positive affect
are also positively correlated with measures of extraversion and
sociability (Costa & McCrae, 1980; Griffin, Mroczek, & Spiro, in
press; Headey & Wearing, 1989). For example, in one experience
sampling study, high school students from the United States and
Italy were beeped several times a day over the course of a week
(Csikszentmihalyi & Wong, 1991). Positive affect reported during
any particular time of day was related to feeling sociable. Harker
and Keltner (2001) found that women who expressed positive
affect in their college photos were more likely to describe themselves as high in affiliation—that is, as warm, cheerful, pleasant,
sociable, understanding, contented, and affectionate—and were
more likely to be described as such by observers.
Social Interaction, Activity, and Energy
The empirical evidence suggests that individuals high in trait
positive affect, happiness, or satisfaction tend to be more social,
active, and energetic. Cross-sectional investigations reveal that
happy and satisfied people report engaging in a greater frequency
of activities, in general (Burger & Caldwell, 2000; Matikka &
Ojanen, in press; Mishra, 1992; Veenhoven, 1994; Watson, Clark,
McIntyre, & Hamaker, 1992), and in social interactions (Gladow
& Ray, 1986; Lucas, 2001; Watson, 1988) and group leisure
activities, in particular (Mishra, 1992). Relative to their less happy
peers, happy individuals also report having stronger social support
(Matikka & Ojanen, in press), attending club meetings more frequently (Bahr & Harvey, 1980; Lebo, 1953), and holding more
organizational affiliations (Bahr & Harvey, 1980; Mishra, 1992).
This flurry of activity does not appear to go unnoticed. Happier
individuals are more likely to be rated as energetic and active by
their families and friends (Diener & Fujita, 1995; Schimmack et
al., 2004).
Okun and colleagues meta-analyzed 556 sources to determine
the relation between happiness and the frequency of social activities (both formal and informal) in older adults (Okun, Stock,
Haring, & Witter, 1984). A positive significant association was
found, regardless of type of activity (e.g., having to do with
voluntary organizations vs. friends) or activity partner (e.g.,
friends vs. neighbors). Furthermore, in a study of men and women
over 60 years old, happiness was related to a desire to learn a new
skill or take a class, an expressed need for a larger number of
cultural and educational activities in their community, and being
informed about politics (Lebo, 1953). Thus, happier people appear
to be not only more social and more active, but also more interested and more informed.
In summary, individuals high in happiness or trait PA appear to
be more likely to approach rewarding activities, especially social
ones, and may even be more sensitive to pleasurable stimuli in
general (such as social interactions or hobbies; Watson, 1988).
Indeed, positive emotionality has been found to relate to approach
goals (Elliot & Thrash, 2002).
What about research relevant to transient mood? The evidence
indicates that positive mood is also related to relatively greater
involvement in activities, more frequent social interactions, and
increased feelings of energy. For example, the results of 2- to
13-week long diary studies reveal that high levels of positive
affect, as reported in daily or weekly logs, are associated with
reports of spending relatively more time socializing with friends,
family, or romantic partners (Lucas, 2001; Watson et al., 1992)
and a greater frequency of being engaged in a variety of activities
(e.g., going to a party, a museum, or out for a meal; going shopping
or on a week-end trip; Cameron, 1975; Watson et al., 1992). In an
experience sampling study, positive affect reported during any
particular time of day was related to feeling alert and active, to
being with friends, and to engaging in a variety of leisure activities
(e.g., sports and games, socializing, and arts and hobbies;
Csikszentmihalyi & Wong, 1991; see also Lucas, 2001).
Enjoyment of Activities and Social Interactions
The data suggest that happy people participate in more activities
than their less happy peers. However, do they derive more satisfaction from them? Extensive correlational evidence indicates that,
indeed, happy individuals are more likely to enjoy their leisure
activities and social interactions, to experience more “flow” states,
and to be more satisfied with their activities in general. Lu and
Argyle (1991) found that happy community-dwelling men and
women were more likely to report enjoying leisure activities and
group activities. Happiness has been found to be significantly
related to satisfaction with one’s leisure and recreational activities
among both students and retirees (Kahana et al., 1995; Lyubomirsky et al., in press; Veenhoven, 1994). Laboratory and diary
studies corroborate these findings—that is, happy individuals appear to have more pleasurable and more successful social interactions with others. When female students were left alone in a room
with a peer, those high in trait PA were more satisfied with the
conversation that resulted; liked their partner more; and reported
that their conversation was more enjoyable, pleasant, smooth,
relaxed, and less awkward and forced than those low in trait PA
(Berry & Hansen, 1996). Kashdan and Roberts (2004) described
very similar findings. Furthermore, in a daily diary study, students
high in trait PA reported engaging in more enjoyable social interactions than their low positive affect peers (Berry & Hansen,
1996). Finally, in a study of working adults, global happiness was
found to be associated with intrinsically rewarding experiences—
that is, activities that the individual wants to be doing for their own
sake (Graef, Csikszentmihalyi, & Gianinno, 1983). The authors
speculated that chronically happy people may be able to perceive
any activity— even routine, commonplace situations—as intrinsically motivating, and therefore discover rewards even in ordinary,
mundane events.
Correlational studies of transient positive moods have also
found such moods to be associated with satisfying social interactions. For example, when engaged in an intimacy-building task,
those experiencing positive affect are relatively more likely to feel
closer and more attracted to their partner (Kashdan & Roberts,
2004). Furthermore, positive moods have been linked with the
intrinsically rewarding state called flow. The concept of flow was
introduced by Csikszentmihalyi (1975, 1997), who described it as
an experience so engrossing and enjoyable, it is worth doing for its
own sake. When in flow, people report feeling enraptured, as
though in a different reality, lacking self-consciousness, and lacking a sense of the passage of time. Indeed, transient positive affect
is often accompanied by flow (Hektner, 1997). For example, the
results of experience sampling studies suggest that hour-by-hour
experiences of positive affect are related to reports of being in flow
(Csikszentmihalyi & Wong, 1991; Hektner, 1997).
827
Likability and Cooperation
Likability
Is there truth in the sentiment that happy people are disliked
because they are shallow and annoyingly cheerful? The existing
cross-sectional studies actually show the reverse pattern of results.
That is, most respondents like happy people much more than they
like their less-than-happy peers. Happy and satisfied individuals
are judged as more physically attractive (Diener, Wolsic, & Fujita,
1995; Mathes & Kahn, 1975); more intelligent and competent
(Diener & Fujita, 1995); more friendly, warm, and assertive
(Schimmack et al., 2004); less selfish (Rimland, 1982); more
moral; and even more likely to go to heaven (King & Napa, 1998).
Diener and Fujita (1995) found that friends and family members of
happy students, relative to those of less happy ones, rated them as
more socially skilled (e.g., more articulate and well mannered),
better public speakers, self-confident, and assertive, and as having
more close friends, a strong romantic relationship, and more family support. These findings were replicated in a recent study, which
found that friends of students relatively high on chronic happiness
evaluated them more favorably relative to their peers on a variety
of attributes and skills, such as academic ability, self-respect,
selfishness, and pretentiousness (Taylor, Lerner, Sherman, Sage, &
McDowell, 2003). Furthermore, happiness in children, as rated by
their teachers, was found to relate to the children’s popularity
(S.-M. Chen, 1980).
In other studies exploring the happiness–likability relation, secondary school and college students read stories about hypothetical
people. Second through eighth graders judged the happiest targets
as the ones most likely to help (Perry, Perry, & Weiss, 1986), and
undergraduates, no matter what mood they themselves were experiencing, judged the happiest targets as being more likable (Bell,
1978). Finally, in laboratory studies, when female students were
left alone with a peer, the partners of the high PA women found the
social interaction to be more enjoyable and of higher quality, and
so did neutral observers of the interaction (Berry & Hansen, 1996);
objective observers rated participants high in dispositional positive
affect as evidencing stronger leadership while performing a management task (Staw & Barsade, 1993).
Happy moods also tend to lead people to appear more appealing
and inviting to possible interaction partners (Veenhoven, 1988).
Support for this assertion comes from an intriguing study by
Harker and Keltner (2001), who rated the positive affect expressed
in yearbook photographs of graduating seniors from a women’s
college. The personalities of the women in the photos were later
judged both by observers (who interacted with them in person
when the women were in their 20s) and by coders (who only
viewed the photos). The observers judged women who had shown
the highest levels of sincere PA (i.e., Duchenne smiles) as relatively higher on the personality dimension of affiliation (e.g.,
generous, considerate, protective of close ones, and capacity for
close relationships) and lower on the dimension of negative emotionality (e.g., not irritable, fearful, or hostile toward others). The
judges, who only had the opportunity to see the photos, inferred
that women with high positive affect were also relatively higher in
affiliation and lower in negative emotionality, as well as being
higher in positive emotionality (e.g., cheerful, sociable, and appreciative of and responsive to humor) and competence (e.g., productive, dependable, and high intellectual capacity). In summary,
828
women who expressed genuine happiness were liked more than
women who looked less happy. Indeed, the judges of the photos
reported that they expected future hypothetical interactions with
the high positive affect women to be relatively more rewarding.
A study by Kashdan and Roberts (2004) corroborated these
findings. Participants were asked to answer a series of questions
with two peers (actually confederates), such that ever-greater levels of self-disclosure and intimacy were required. The higher the
participants’ levels of PA, the more likely that the confederates felt
themselves interpersonally closer and more attracted to them.
It is worth noting that, although the correlational studies described in this section cannot definitively establish causality, the
causal direction is unlikely to flow solely from likability to happiness. That is, studies in which informants rate hypothetical
targets or fresh acquaintances are unlikely to suggest that the
informants’ high ratings are the cause of the target’s happiness.
Negotiation and Conflict Resolution
Are happy people or those experiencing pleasant moods superior at resolving conflict? The majority of research in this area
involves laboratory experiments, which will be described later. To
our knowledge, the only study to examine conflict resolution in
chronically happy people was an investigation of the CEOs of 62
U.S. companies and their top managers. The results of this study
revealed that work groups whose members were high in average
trait PA were less likely to experience conflict and more likely to
cooperate (Barsade, Ward, Turner, & Sonnenfeld, 2000). Furthermore, a correlational study of PA—also conducted in a work
setting—found that the experience of particular positive emotions
at the office is related to reduced conflict with colleagues (Van
Katwyk et al., 2000).
affect also related to generosity? The few studies in this area
support an affirmative conclusion. For example, according to the
results of experience sampling and diary studies, the percentage of
time spent in a good mood was associated with self-reported
altruism among school-age youths (Csikszentmihalyi, Patton, &
Lucas, 1997) and with amount of time spent helping others among
college undergraduates (Lucas, 2001). An investigation of high
school students found that those who reported having the most
intense positive experiences were relatively more likely to be
involved in community service activities (e.g., volunteering) and
reported more desires to contribute to society and to be of assistance to others (Magen & Aharoni, 1991, also cited previously).
Finally, in a study of preschoolers, those who displayed happy
moods most frequently were also most likely to show empathy
toward others—for example, by providing positive reinforcement
or comfort (Strayer, 1980).
Research with working adults has shown similar effects. For
example, positive affect experienced at work has been related to
intentions to perform behaviors that are beyond the call of duty
(Williams & Shiaw, 1999), even after controlling for such variables as demographics and trait positive affect. Positive moods
experienced at work have also been related to actual prosocial
organizational behavior. Even after controlling for dispositional
affect, positive affect in salespeople predicted more helping of
customers and more customer service, as well as more extrarole
prosocial behavior on the job (George, 1991).
In summary, cross-sectional investigations suggest that happy
people are inclined to be kind and charitable people. Furthermore,
these findings dovetail with the work on links between happiness
and interpersonal relationships. If happy people are more altruistic,
they will be liked more, will profit more from future social
interactions (i.e., through the norm of reciprocity), and will have
stronger and more supportive social networks.
Prosocial Behavior
“The good life, as I conceive it, is a happy life. I do not mean that if
you are good you will be happy; I mean that if you are happy you will
be good.”
—Bertrand Russell
In reviewing the cross-sectional research on prosocial behavior,
we address the question whether happy people, as well as those
experiencing pleasant moods, are inclined to be more altruistic,
generous, and charitable people.
Individuals who score high on happiness or trait PA report in
correlational questionnaire studies a relatively greater interest in
helping people (Feingold, 1983), a tendency to act in a prosocial or
cooperative manner (e.g., as enjoying sharing or helping others;
Rigby & Slee, 1993), and intentions to perform specific altruistic,
courteous, or conscientious behaviors at work (e.g., helping a
colleague with work problems despite one’s own heavy workload;
Williams & Shiaw, 1999). Happy people also report having performed more altruistic acts in the recent past (e.g., shopping for a
sick friend or stopping to help a stranger; Krueger et al., 2001).
Furthermore, in two studies that tracked subjects’ behaviors from
once to seven times a day, students high in trait PA reported
spending a relatively greater percentage of their time helping
others (Lucas, 2001).
Have similar effects been found in cross-sectional investigations
of transient mood—that is, is the day-to-day experience of positive
Physical Well-Being and Coping
The literature on physical well-being, healthy behavior, and
coping has not generally focused on positive affect or positive
experience (for some notable exceptions, see Antonovsky, 1988,
1993; Seligman, 1991; Snyder, 2000). Instead, the overwhelming
majority of research in this area has examined the effects of
hostility, stress, depression, and anxiety (Kubzansky & Kawachi,
2000). Thus, few studies on health have measured well-being,
although some have included assessments of such related constructs as optimism, hope, and sense of humor. Next, we review
the small number of relevant correlational studies available in this
area.
Health Perceptions
Happy people consistently report themselves as healthier. Relative to their less happy peers, happy respondents rate themselves
higher in global health (e.g., Achat et al., 2000; Kehn, 1995;
Lyubomirsky et al., in press; Mroczek & Spiro, 2005; Røysamb et
al., 2003; Stones & Kozma, 1986, as cited previously) and report
higher levels of social and physical functioning (Pinquart & Sörensen, 2000) and lower levels of pain (Achat et al., 2000;
Røysamb et al., 2003, as cited earlier). Additionally, well-being is
related to higher rates of patient compliance, a predictor of good
health (DiMatteo, Lepper, & Croghan, 2000). However, because
positive self-report biases could account for these findings, they
must be considered together with other data on the health and
coping abilities of happy people (see next). Furthermore, it is
notable that Watson (2000) reported small but positive correlations
between measures of positive emotionality (extraversion and positive temperament) and injury visits to a university health center
(rs ⫽ .12 and .15)—suggesting, perhaps, one of the downsides of
the active lifestyle of happy folk. In addition, extraversion and
positive temperament were related to more health visits for illness
(rs range from .15 to .17) in two samples. These modest correlations may suggest that extraverts show a “readiness to use services” (Rosenstock & Kirscht, 1979).
Not surprisingly, high positive affect and low negative affect
have also been associated with subjective reports of better health
(Benyamini, Idler, Leventhal, & Leventhal, 2000; Pettit, Kline,
Gencoz, Gencoz, & Joiner, 2001; Sullivan, LaCroix, Russo, &
Walker, 2001) and fewer physical symptoms (Watson, 1988; Weinglert & Rosen, 1995; though Watson & Pennebaker, 1989, found
no relation between PA and symptom reports [rs between –.19 and
.04]). In one investigation, individuals who worked hard to maintain their happy moods reported fewer illnesses (Goldman, Kraemer, & Salovey, 1996).
Healthy Behavior
As delineated previously, research evidence reveals that chronically happy people are relatively more energetic and more involved in a variety of social, recreational, occupational, and physical activities (e.g., Mishra, 1992; Riddick, 1985; Veenhoven,
1994; Watson, 1988; Watson et al., 1992). Happy individuals are
also less likely to engage in a variety of harmful and unhealthy
behaviors, including smoking, unhealthy eating, and abuse of
drugs and alcohol (e.g., Graham et al., in press; Piko, Gibbons,
Luszcynska, & Teközel, 2002). Within the Big Three approach to
personality (L. A. Clark & Watson, 1999), psychoticism and
neuroticism tend to be viewed as most relevant to addictions (e.g.,
Eysenck, 1997), although it is notable that drug addicts tend to
have relatively lower scores on extraversion (see Eysenck, 1997,
for a review). L. A. Clark and Watson (1999) likewise reported
that disinhibition (rather than negative or positive temperament)
tends to be related to drug use, smoking, drinking alcohol, and
positive attitudes about promiscuous sex. Thus, research has
tended to support the notion that, to the extent that positive
emotionality is relevant to the question of addictions and risky
behavior, it is related to lowered levels of these unhealthy states.
Although the data are quite limited, short-term positive emotions also appear to be associated with illness preventative and
health promotive behaviors and behavioral tendencies. For example, recent positive happy moods were associated with less cigarette use and alcohol intake (Pettit et al., 2001) and with higher
sleep quality and quantity (Bardwell, Berry, Ancoli-Israel, &
Dimsdale, 1999). Furthermore, in addition to its links with higher
levels of activity and energy, positive affect is positively correlated
with higher levels of physical exercise (Lox, Burns, Treasure, &
Wasley, 1999; Watson, 1988). In a study of women at moderate
risk for breast cancer, positive affect predicted engagement in
physical activity, particularly during leisure time (Audrain,
Schwartz, Herrera, Golman, & Bush, 2001). Physical activity, in
829
turn, is associated with many positive health outcomes (Fraser &
Shavlik, 2001; Shephard, 1993). Thus, positive affect might benefit health by indirect relations to health promoting activities.
Immunity
The evidence described previously suggests that happiness and
positive affect may be associated with enhanced physical wellbeing because of their relation to such variables as physical exercise and social support, which, in turn, are linked to improved
health. However, might long-term happiness and short-term positive affect also have direct effects on health? One mechanism
through which psychological states impinge on physical health
directly is through their effects on the immune system. Immune
system disruption has been implicated in the etiology and progression of a wide array of illnesses (cf. Baum & Poluszny, 1999;
Kiecolt-Glaser, McGuire, Robles, & Glaser, 2002).
Before highlighting the research on transient mood, we first
address whether happy people—that is, those who experience
habitual positive moods—might show enhanced immune function.
Unfortunately, direct evidence on this question is practically nonexistent. Certainly, research supports the notion that the chronic
absence of positive affect is related to immune deficiency. Longterm deficits in positive mood—that is, sadness or depressive
symptoms—are associated with decreased lymphocyte production
(McGuire, Kiecolt-Glaser, & Glaser, 2002).
Individuals with attributes closely related to happiness have also
been found to show heightened immunocompetence. For example,
in two separate investigations, humor was associated with enhanced immune function in participants who were predisposed to
use humor as a routine coping device (Dillon, Minchoff, & Baker,
1985; Dillon & Totten, 1989). Furthermore, sense of coherence
(Antonovsky, 1993) was associated with enhanced natural killer
(NK) cell activity among older adults facing the stress of relocation (Lutgendorf, Vitaliano, Tripp-Reimer, Harvey, & Lubaroff,
1999). Finally, in another relevant study, optimism was negatively
associated with incidence of upper respiratory infection (Lyons &
Chamberlain, 1994).
Research on naturally occurring moods corroborates these findings. It should be noted, however, that results linking positive
mood with immunity are rarely straightforward and depend on the
levels of negative affect experienced, as well as on the particular
immune measure used in a given study (Booth & Pennebaker,
2000). In one investigation, immunity was elevated on days when
positive mood predominated and was reduced on days when negative mood predominated (Stone, Cox, Valdimarsdottir, Jandorf, &
Neale, 1987). Controlling for negative mood, however, the effect
for positive mood fell to marginal significance. In a similar study,
Stone and colleagues (1994) found enhanced antibody activity on
days with more positive than negative moods. The experience of
uplifts during daily hassles has been related to heightened NK cell
activity in individuals with cancer histories (Vitaliano et al., 1998;
see also Lyons & Chamberlain, 1994). Other research has also
shown a relation between positive affect and NK cell activity. For
example, naturally occurring positive mood was found to be associated with higher levels of NK cells, but only among women
who reported some negative mood (Valdimarsdottir & Bovbjerg,
1997). These results may indicate a possible buffering of the
effects of negative mood by positive moods (Booth & Pennebaker,
830
2000), or they may distinguish the healthy happy from the repressive happy.
Coping
Do long-term and short-term PA also play a positive role in
effective coping with stress and challenge? If yes, this finding may
elucidate another pathway through which happiness and positive
emotions might have salutary effects on health. We explore this
issue in this section.
Cross-sectional research pertaining to the question of whether
happy people are better copers is primarily indirect, although a few
studies have assessed global happiness. For example, McCrae and
Costa (1986) examined personality variables as predictors of effective coping. They concluded that positive affectivity, or trait
PA, was associated with more effective and more “mature” coping
efforts. A strong correlation has also been found between positive
emotionality and coping by active engagement (Miller & Schnoll,
2000).
A number of constructs have been introduced in the coping
literature to explain the capacity of some individuals to maintain a
positive outlook during negative life circumstances. Optimism
(defined as attributional style, Seligman, 1991, or as general positive expectancy, Carver & Scheier, 2001), sense of coherence
(Antonovsky, 1988, 1993), hope (Snyder, 2000), and hardiness
(Maddi & Kobasa, 1991) all refer to general traits that are correlated with happiness and promote positive feelings during difficult
circumstances, and all have been linked with important health
outcomes (e.g., Maruta, Colligan, Malinchoc, & Offord, 2000;
Peterson, Seligman, Yurko, Martin, & Friedman, 1998; Snyder,
2000). For example, research has demonstrated that optimistic
individuals attend to and remember potentially threatening healthrelevant information more than pessimistic ones (Aspinwall, 1998;
Aspinwall & Brunhart, 1996) and use humor and positive reframing, instead of denial, when coping with highly stressful events
(Carver et al., 1993). A similar construct— hope—was also found
to relate to adaptive coping with cancer (Irving, Snyder, & Crowson, 1998). One possibility is that the effects of these constructs on
positive mood mediate their relations to physical health outcomes
(as has been shown by Segerstrom, 2000, in the case of optimism).
Although these concepts differ in a variety of ways, their correlations with positive affect are well established (cf. L. A. Clark &
Watson, 1991).
To the extent that happiness predisposes people to look on the
bright side, it should relate to superior coping during difficult
times. For example, in one study, happy students reported experiencing similar types of both positive and negative life events as
did their less happy peers, but the happy students tended to think
about both types of events more favorably and positively—for
example, by seeing humor and didactic value in adversity and by
emphasizing recent improvement in their lives (Lyubomirsky &
Tucker, 1998). In summary, research supports the argument that
the extent to which a person can maintain sensitivity to pleasurable
opportunities, even in difficult times, may be highly adaptive.
Research in a variety of areas demonstrates that positive experience and positive emotion, even in the midst of stress or challenge, may be associated not with maladjustment and denial, but,
rather, with particularly good outcomes. Indeed, Aspinwall (1998)
has argued cogently for the recognition of positive affect as a
resource in coping and self-regulation. Thus, although previous
models viewed positive affect as primarily sending a “maintain”
message to the goal seeker (e.g., Carver & Scheier, 1981, 1990),
Aspinwall pointed out the role of positive affect in harnessing
attention even to negatively tinged information.
A number of studies are consistent with this argument. Keltner
and Bonanno (1997) showed that the expression of genuine positive emotion (particularly Duchenne laughter) during bereavement
relates to heightened adjustment on a variety of levels. Fredrickson
and colleagues found that positive emotions undo the effects of
negative emotion on cardiovascular function (Fredrickson & Levenson, 1998; Fredrickson, Mancuso, Branigan, & Tugade, 2000).
A study of coping with stress following the September 11, 2001,
attacks in the United States found that resilient individuals were
less likely to experience depression and more likely to report
increases in psychological growth after the attacks (Fredrickson,
Tugade, Waugh, & Larkin, 2003). Of importance, positive emotions experienced after the attacks completely mediated the relation between resilience and coping variables. Finally, Pennebaker
(1993) found that those who used relatively more positive than
negative emotion words while writing expressively during difficult
or distressing times were most likely to benefit from disclosive
writing (cf. Pennebaker & Francis, 1996; Pennebaker, Mayne, &
Francis, 1997). Thus, the experience of the positive in the context
of traumatic or negative events has implications for psychological
and physical well-being. These moments of positive emotion may
be viewed as opportunities to replenish one’s system, which has
been depleted by grief (cf. Folkman & Moskowitz, 2000, for a
similar view).
Creativity and Problem Solving
“The happiest people are those who think the most interesting
thoughts.”
—William Lyon Phelps
Flexibility and Originality
The few existing correlational studies in this area suggest that
chronically happy people score higher on measures of creativity.
For example, relatively higher scores on tests of creativity have
been documented in happy, relaxed, and bold children (Cacha,
1976) and in individuals with hypomanic personality traits (Schuldberg, 1990). Certain personality characteristics such as flexibility
and openness are related to creativity, and these traits are most
common among those who have periods of hypomania without
depression, but are not as common in those who have hypomania
with intermittent depression (Shapiro & Weisberg, 1999). Notably,
the most creative group in the Shapiro and Weisberg study was
composed of individuals with elevated moods and without symptoms of depression. Furthermore, eminently creative people have
been shown to be characterized by dominance and self-confidence
(Feist, 1998)—two facets of extraversion to be consistently related
to long-term well-being (e.g., Lucas et al., 2000). However, Openness (the fifth major factor in the Big Five Factor Model) is usually
not related to chronic happiness (McCrae & Costa, 1991; rs ⫽ .01
and ⫺.05), suggesting that the connection between happiness and
flexibility is at the momentary level, not necessarily at the longterm level of personality.
Obviously, research on whether happy individuals are relatively
more flexible is scarce, and much more research is needed to draw
firm conclusions in this area. Fortunately, studies of naturally
occurring moods—although also rare— corroborate these findings.
For example, Richards (1994) described “everyday creativity” in
which people find new ways to approach activities and problems in
their daily lives. She found that everyday creativity occurred when
people were in a normal or elevated mood, and rarely when they
were depressed. Positive moods—particularly those involving
high-arousal emotions such as excitement or joy—are also related
to curiosity and desire for exploration (Kashdan, Rose, & Fincham,
2004). In a field study, positive affect expressed by employees on
the job was correlated .30 with supervisors’ evaluations of the
employees’ creativity (Staw et al., 1994). Finally, mildly manic
states have been found to be associated with creative episodes in
such fields as poetry (Richards, 1994; Richards & Kinney, 1990;
cf. Jamison, 1990).
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positive construals of self and other (mean r ⫽ .39), sociability and
activity (mean r ⫽ .33), prosocial behavior (mean r ⫽ .32),
popularity (mean r ⫽ .31), healthy behavior (mean r ⫽ .33), high
immune functioning (mean r ⫽ .33), and good coping with distress
(mean r ⫽ .34). The evidence, although less conclusive, also
suggests that chronically happy people and those in pleasant
moods might be more creative as well as more efficient problem
solvers (mean r ⫽ .26) and might show superior conflict resolution
skills (mean r ⫽ .23). Most, if not all, of these attributes appear to
promote active goal involvement, which is adaptive in many
circumstances and likely facilitates success in a broad range of life
domains. However, additional research is needed in many of the
areas we reviewed, because only a handful of studies are available
that are relevant to certain domains.
LONGITUDINAL EVIDENCE
Question 3: Does Happiness Precede Success?
Performance on Complex Mental Tasks
Surprisingly, few cross-sectional studies have examined the
performance of chronically happy people on complex mental tasks.
One exception is an investigation by Staw and Barsade (1993),
who had participants perform a manager assessment task that was
based on a complex 3-hr “in-basket task”—that is, a procedure
assessing a person’s ability to effectively complete a series of
diverse assignments. As rated by objective observers, those high in
dispositional positive affect performed better on the manager assessment task—for example, they received relatively higher scores
on mastery of information. In another set of studies, after being led
to believe that they “failed” on an earlier task, chronically happy
individuals showed superior performance, relative to less happy
individuals, on the reading comprehension portion of the Graduate
Record Examination (Lyubomirsky, Kasri, Zehm, & Dickerhoof,
2005).
Research on choice and decision making, albeit indirect, further
suggests that happy people make better and more efficient decision
makers. For example, people high in well-being appear to be less
susceptible to negative influences from advertising (Geier,
Schwartz, & Brownell, 2003). Happy individuals are also more
likely than their unhappy peers to optimize or satisfice in their
decision making, rather than maximizing to achieve the best outcome regardless of the cost in time and effort (B. Schwartz et al.,
2002). That is, unhappy people carefully search for the very best
answer, regardless of the importance of the context or the novelty
of the problem. This effortful strategy is likely to be inefficient in
situations that have been frequently encountered in the past and for
which a reasonable solution has been found, as well as in much of
life in which time is limited and tasks are complex.
Is positive affect also associated with enhanced performance on
complex mental tasks? The overwhelming majority of the literature in this area consists of experimental studies; hence, these
investigations are discussed in a later section.
Conclusion
In this section, we reviewed cross-sectional evidence suggesting
that both long-term well-being and momentary positive affect are
associated with a number of desirable characteristics, including
Our review of the cross-sectional literature revealed how extensive and robust are the correlations found between chronic happiness, as well as short-term positive affect, and numerous indicators
of culturally valued success, including successful outcomes in
work, relationships, and health, as well as characteristics and
behaviors paralleling these outcomes. However robust and wideranging the correlations we observed, they offer only preliminary
evidence that a causal relationship might exist between happiness
and success. In the next two sections, we consider the much
smaller number of longitudinal studies, which increase our confidence—though not definitively—in the notion that long-term happiness and short-term positive moods might actually cause the
outcomes with which they correlate. The relevant study characteristics are presented in the first half of Table 2, and the effect sizes
are displayed in Table 2 and in the middle panel of Table 4.
Work Life
Employment and Quality of Work
Longitudinal studies corroborate the correlational literature linking happiness and desirable work outcomes. For example, people
with high subjective well-being who are interviewing for a job are
relatively more likely to receive a callback second interview 3
months later (Burger & Caldwell, 2000). A study by Roberts,
Caspi, and Moffitt (2003) is instructive regarding the causal direction between happiness and work outcomes because measures
of positive affect were collected at age 18 and the work outcomes
were assessed at age 26. Positive affectivity at the end of adolescence predicted outcomes such as financial independence, occupational attainment, and work autonomy in young adulthood.
However, positive job characteristics also led to increases in positive affect, suggesting a bidirectional influence. Paralleling these
findings, in a prospective longitudinal study, employees high in
dispositional positive affect had jobs 1.5 years later that allowed
them more autonomy, meaning, and variety (Staw et al., 1994).
Furthermore, Verkley and Stolk (1989) found that people who
were happy were less likely to lose their jobs in the ensuing period.
Additionally, unemployed individuals with high subjective wellbeing were more likely to be reemployed at follow-up than unhappy people. Although the effects of subjective well-being were
832
small in both directions in this study, they persisted when factors
such as length of unemployment and health were statistically
controlled.
Once employed, those who show high positive affect on the job
receive relatively more favorable evaluations from supervisors—
for example, for quality of work, productivity, and dependability—a year and a half later (Staw et al., 1994). Happy people have
also been shown to receive higher supervisory ratings over time in
other studies (Cropanzano & Wright, 1999; Wright & Staw, 1999).
For example, happier employees were rated by their administrative
officers as superior up to 3.5 years later in the four dimensions of
support, work facilitation, goal emphasis, and team building
(Wright & Staw, 1999, Study 1). Positive affect on the job has also
been found to predict reduced absenteeism 5 months later (Pelled
& Xin, 1999). Finally, a construct related to positive affect, dispositional optimism, predicted the success of life insurance agents
(Seligman & Schulman, 1986).
Income
Longitudinal and prospective studies of the link between happiness and income are more persuasive regarding causal direction
than cross-sectional investigations. For example, in an Australian
panel study, young adults who described themselves as happy
during a particular period of time were more likely to increase in
income during the following period (Marks & Fleming, 1999).
Similar results were obtained in a Russian panel study, in which
individuals’ happiness levels in 1995 were positively correlated
with higher income and lower unemployment in 2000, even after
controlling for other demographic variables (Graham et al., in
press).
Diener and his colleagues related measures of cheerfulness as
students entered college to the respondents’ income when they
were in their 30s (Diener, Nickerson, Lucas, & Sandvik, 2002).
Students with greater cheerfulness in the first year of college
earned more money 16 years later, and this effect persisted even
after controlling for their parents’ income. The effect of cheerfulness was strongest for the respondents whose parents were well
off. When the parents’ income was high (i.e., above $50,000 U.S.),
the most cheerful college students later made $25,000 more per
year than did the least cheerful college students. Furthermore, the
cheerful college students were less likely to experience long-term
unemployment after college. Corroborating these results, in a
study of working adults, employees who were high in dispositional
positive affect received greater pay increases over time than those
low in positive affect (Staw et al., 1994). These data suggest that
high subjective well-being leads to later financial prosperity.
Social Relationships
Friendship, Social Support, and Marriage
While a number of longitudinal studies have examined marriage, we are aware of only one longitudinal investigation relevant
to social support. In the workplace, employees who showed high
dispositional positive affect received more emotional and tangible
assistance more than a year later (Staw et al., 1994). This study
suggests that happy people may draw others to help and befriend
them.
As described earlier, numerous cross-sectional investigations
have documented a link between happiness and the state of being
married. However, the most powerful evidence for the argument
that happiness leads to marriage, as opposed to the reverse, comes
from several longitudinal investigations (Lucas, Clark, Georgellis,
& Diener, 2003; Marks & Fleming, 1999; Spanier & Furstenberg,
1982; see also Neyer & Asendorpf, 2001). Marks and Fleming
(1999) conducted a longitudinal study with four cohorts of nationally representative young Australians, who participated every 1 to
2 years over the course of a total of up to 15 years. Unmarried
respondents who were one standard deviation above the mean on
happiness were 1.5 times more likely to be married at a later time
period than those with mean levels of happiness. Unmarried respondents two standard deviations above the mean on happiness
were twice as likely to be married later. A 16-year panel study
carried out in Germany corroborated these results (Lucas et al.,
2003). German men and women who were highly satisfied with
their lives were more likely to get married 4 or more years later
than those who were initially less satisfied. Finally, U.S. women
who expressed sincere positive affect in their college yearbook
photos at age 21 were relatively more likely to be married by age
27 and less likely to remain single through middle adulthood
(Harker & Keltner, 2001).
Longitudinal investigations also bolster the cross-sectional findings regarding relationship satisfaction, suggesting that individual
happiness may bolster marital well-being (Headey et al., 1991;
Headey & Veenhoven, 1989; Ruvolo, 1998; Spanier & Furstenberg, 1982). For example, in a 6-year, four-panel study of Australians, participants’ individual happiness at earlier time periods
increased the likelihood that they would have a happy marriage at
later time periods (Headey & Veenhoven, 1989). Furthermore, in
a 2.5-year prospective study, Spanier and Furstenberg (1982)
found that happier people were more likely to remarry after a
divorce. An even longer term result was reported in the previously
mentioned study of women’s college yearbook photos (Harker &
Keltner, 2001). The amount of positive affect expressed in these
photos at age 21 predicted marital satisfaction 31 years later.2
Finally, an intriguing finding is that people’s global happiness at
one point in time can potentially influence the marital well-being
of their spouses a year later (Ruvolo, 1998).
Self-esteem has also been related to relationship quality in
longitudinal research. For example, German adults, ages 18 to 30
years, reported on several characteristics of their social relationships at two time periods (Neyer & Asendorpf, 2001). Respondents’ levels of self-esteem at Time 1 predicted, over the course of
four years, increases in the closeness and importance of their social
relationships (especially those with friends and colleagues) and
decreases in the feelings of security and amount of conflict participants had in their relationships.
2
These conclusions must be tempered in light of a meta-analysis by
Karney and Bradbury (1995). In their review of 115 longitudinal studies of
marital stability and satisfaction, extraversion was found to have a weak
negative relationship to marital stability (average r ⫽ ⫺.04 for wives, ⫺.08
for husbands, and ⫺.08 for couples). However, these results were based on
1 or 2 studies and the measures of extraversion used were not specified.
Why Might Happier People Reap Larger Social Rewards?
Both the cross-sectional evidence and the longitudinal evidence
we have reviewed thus far strongly suggest that happy people are
better able to develop social relationships and build a rich network
of support. To quote Wilson’s (1967) oft-cited review of the
literature on well-being, “Perhaps the most impressive single finding lies in the relation between happiness and successful involvement with people” (p. 304). Furthermore, the social rewards of
happiness cannot be overstated, as strong social bonds and social
support have been shown to further elevate positive emotions and
enhance social acceptance, health, and emotional adjustment (Argyle & Martin, 1991; Cohen, 1988; House, Landis, & Umberson,
1988; Myers, 1992) and, as some writers have suggested, even to
aid human survival (Berscheid, 2003; Myers, 1999).
We submit that the primary mechanism underlying the relation
between long-term happiness and the quality and quantity of social
relationships is the experience of frequent positive emotions. A
growing body of research suggests that happy feelings have a
marked positive influence on interpersonal behavior (Isen, 1999).
People are more likely to want to become friends with and to
provide emotional and tangible assistance to individuals with a
positive outlook (Salovey, Rothman, Detweiler, & Steward, 2000).
For example, an observational study of children found that displays of happy affect were most frequently responded to by other
kids with empathic behaviors—for example, positive reinforcement and physical or verbal comfort (Strayer, 1980). Befriending
and supporting a happy person may evoke positive feelings in the
friend or helper, increasing the likelihood that they will seek to
maintain the relationship (Staw et al., 1994). In Gottman’s work on
happy marriages, he has found that the longevity of marriages is
best predicted by the ratio of positive versus negative interactions
(Gottman & Levenson, 1999).
Furthermore, genuine Duchenne smiles and laughter, which
characterize chronically happy people, signal to other people that
one is friendly and open to social interaction, inviting them to
become engaged (Frijda & Mesquita, 1994; Keltner & Kring,
1998; Ruch, 1993). This argument is supported by theory and
research on the role of positive emotions in infant– caregiver
interactions. Smiles in infants appear to build infant– caregiver
attachment, ensuring that the caregiver will continue to care for the
child and secure his or her well-being (Fredrickson, 1998, 2001;
Tomkins, 1962). Mothers who express positive emotions have
infants who begin to express positive emotions as well (e.g.,
Haviland & Lelwica, 1987). Indeed, such observations led Eric
Fromm (1962) to go so far as to argue that “a mother must not only
be a ‘good mother,’ but also a happy person” (p. 49).
Mental health. Although few longitudinal studies address issues of mental health, the existing evidence parallels the crosssectional data. For example, a longitudinal investigation showed
that people who were high in subjective well-being were less likely
to suffer depressive symptoms if they became unemployed in the
ensuing period (Verkley & Stolk, 1989). Furthermore, in a longitudinal study of the Finnish Twin cohort, life satisfaction was
associated with lower risk of work disability that was due to
psychiatric causes (as well as nonpsychiatric ones) from 1 to 12
years later (Koivumaa-Honkanen et al., 2004). Within the same
sample, subjective well-being was also shown to be negatively
associated with suicide. That is, life satisfaction was found to
833
relate to a lower risk of suicide 20 years later, even after controlling for other risk factors such as substance use, smoking, physical
activity, age, and sex (Koivumaa-Honkanen et al., 2001). An
additional relevant study concerns substance use, another indicator
of poor mental health. In a longitudinal study of more than 1,700
adolescents, positive affect buffered the relation between negative
affect and substance use (Wills, Sandy, Shinar, & Yaeger, 1999).
Finally, a construct related to well-being— optimistic attribution
style—was found to predict mental health problems, poor adjustment, and high levels of drinking 10 years later (Peterson et al.,
1998).
Physical health. Longitudinal studies are also useful in giving
researchers a process picture of the relation between happiness and
physical health. For example, in a study of close to 5,000 individuals, happiness was related to relatively better health (as measured
by self-reported health problems, days missed that were due to
illness, and hospitalization) 5 years later (Graham et al., in press;
see also Koivumaa-Honkanen et al., 2004). The experience of
positive mood predicted a lower incidence of stroke 6 years later,
especially in men (Ostir, Markides, Peek, & Goodwin, 2001), as
well as sports-related injuries during the course of hockey season
(A. M. Smith, Stuart, Wiese-Bjornstal, & Gunnon, 1997). A related construct— optimism—was also associated with lowered incidence of cardiovascular disease (e.g., heart attacks and angina)
10 years later (Kubzansky, Sparrow, Vokonas, & Kawachi, 2001),
with higher quality of life, heightened physical recovery, and
quicker return to normal behaviors 6 months following cardiac
surgery (Scheier et al., 1989), and with better risk reduction for
cardiovascular heart disease 8 months after surgery (Fitzgerald,
Prochaska, & Pransky, 2000). Shorter term longitudinal studies
mirror these results. For example, low positive affect in adolescents predicted greater delinquent activity 6 months later (Windle,
2000). Furthermore, in two investigations, increasing numbers of
positive events were negatively associated with incidence of upper
respiratory infection over a 2-week period (Lyons & Chamberlain,
1994). An even shorter term study showed that positive mood
assessed on a particular day predicted fewer emergency room and
hospital visits on the next day (Gil et al., 2004).
Longevity and survival. Is happiness associated with superior
longevity and survival? A number of studies—all longitudinal, by
definition— have shown that happy people are less likely to die of
certain causes. In a study of more than 37 nations, subjective
well-being was negatively related to automobile fatalities (Kirkcaldy & Furnham, 2000). A recent study of Scandinavians found
that over a 19-year period, dissatisfaction with life predicted fatal
unintentional injuries as well as intentional injuries (KoivumaaHonkanen, Honkanen, Koskenvuo, Viinamaeki, & Kaprio, 2002).
Research on the influence of emotions on longevity has primarily
stressed the role of negative emotions in decreasing survival times
(e.g., Denollet & Brutsaert, 1998; Naughton et al., 2002). However, studies have also demonstrated longer survival times, after an
illness, for people with positive emotional traits. Individuals experiencing end-stage renal disease who scored highly on overall
happiness were more likely to survive 4 years later (Devins, Mann,
Mandin, & Leonard, 1990), women experiencing a recurrence of
breast cancer who reported joy were more likely to survive 7 years
later (S. M. Levy, Lee, Bagley, & Lippman, 1988), and those
individuals with spinal cord injuries reporting greater satisfaction
with their lives were more likely to survive 11 years later (Krause,
834
Sternberg, Lottes, & Maides, 1997). Additionally, a longitudinal
study using a sample of 513 Berlin residents revealed a significant
link between well-being and all-cause mortality (Maier & Smith,
1999). Because happiness is associated with a variety of life
outcomes related to survival (e.g., stable relationships, lower accident and suicide rates, superior coping, and less stress; Baum &
Poluszny, 1999; House et al., 1988), it would not be surprising if
sustained levels of positive affect should relate to overall longevity. A few large-scale prospective studies have examined the link
between positive traits and longevity. Levy and colleagues examined attitudes about aging in older adults (B. R. Levy, Slade,
Kunkel, & Kasl, 2002). Individuals with positive self-perceptions
of aging lived on average 7.5 years longer than those with less
positive perceptions, even after controlling for age, sex, socioeconomic status, loneliness, and functional health. Notably, the effect
for positive aging attitudes surpassed the effects for body mass,
smoking, and exercise. In a study of older individuals, 12% died
over the course of 2 years, but those rated as happy were significantly less likely to die than those rated as unhappy (Zuckerman,
Kasl, & Ostfeld, 1984; see also Pitkala, Laatkonen, Strandberg, &
Tilvis, 2004). Palmore (1969) investigated predictors of longevity
in a sample of 268 older adults. To control for age effects, the
author examined the number of years a person had survived
compared with the number of years he or she would be expected
to live. This “longevity quotient” was correlated .26 with
interviewer-assessed happiness. Of importance, the objective happiness rating was the second strongest predictor of longevity—
weaker than work satisfaction, but stronger than physical functioning and tobacco use. Corroborating these findings for the link
between happiness and longevity, Deeg and van Zonneveld (1989)
showed that a 70-year-old man of average health is expected to
live 20 months longer if he reports being satisfied with his life one
standard deviation higher than his peers. Danner and his colleagues
(2001) examined predictors of longevity in a large longitudinal
sample of Roman Catholic nuns. Higher levels of positive emotion
expressed in autobiographies written at an average age of 22 were
associated with a 2.5-fold difference in risk of mortality when the
nuns were in their 80s and 90s. These results are impressive given
that the environments of these nuns are expected to be quite similar
throughout their lives, and that other health relevant variables,
such as diet and activity levels, might be assumed to be relatively
homogeneous in this sample. In a study of 2,000 older Mexican
Americans (Ostir, Markides, Black, & Goodwin, 2000), positive
emotionality significantly predicted survival 2 years later, even
after controlling for such variables as marital status, diet, smoking,
and negative affect. Finally, it is notable that studies examining
optimism parallel these results. For example, optimism was associated with lower risk of death for 800 patients followed for 30
years (Maruta et al., 2000; see also Peterson et al., 1998), and men
with an optimistic explanatory style were less likely to die of
coronary heart disease 10 years later (Kubzansky et al., 2001). In
contrast to these recent studies, the well-known Terman Study of
gifted individuals found that a childhood measure of cheerfulness
(rated by parents and teachers) was associated with earlier death
(Friedman et al., 1993). However, the sample used in this study
showed little variability in happiness—most were extremely
happy. Thus, the results might reflect the fact that above some high
level, happiness might not be adaptive. However, this conjecture is
obviously speculative. As it stands, we are uncertain why the
results of the Terman Study diverge from other findings in this
area.
Conclusion
In summary, although the longitudinal literature is much less
extensive than the correlational work, it is still impressive in the
robustness and consistency of its results. Study after study shows
that happiness precedes important outcomes and indicators of
thriving, including fulfilling and productive work (mean r ⫽ .24),
satisfying relationships (mean r ⫽ .21), and superior mental and
physical health and longevity (mean r ⫽ .18). However, relatively
few longitudinal studies were identified altogether, and none were
found in the areas of citizenship and friendship, indicating a clear
need for future research. In the next section, we continue our
review of the longitudinal literature, examining studies that relate
short- and long-term happiness at Time 1 with resources and
characteristics paralleling successful outcomes at Time 2.
Question 4: Do Happiness and Positive Affect Precede
Behaviors Paralleling Success?
Positive Self-Perceptions
To our knowledge, the only relevant longitudinal investigations
in this area concern the link between life satisfaction and positive
affect, respectively, to self-perceptions. The first study indicates
that high life satisfaction can lead to feelings of self-confidence.
Using a panel design, Headey and Veenhoven (1989) investigated
the direction of influence between life satisfaction and feelings of
superiority, and found evidence for causality in both directions.
That is, feeling above average on a number of characteristics
preceded higher life satisfaction, but high life satisfaction was also
followed by greater feelings of superiority. The second relevant
investigation revealed that women who expressed positive affect at
age 21 were relatively more likely to rate themselves high in
competence two to three decades later (Harker & Keltner, 2001;
mean r ⫽ .25).
Longitudinal studies reinforce the cross-sectional findings linking happiness and extraversion, demonstrating that this link holds
even when the two variables are measured many months or years
apart (Costa & McCrae, 1980; Costa, McCrae, & Norris, 1981;
Headey & Wearing, 1989). Longitudinal studies have also shown
that levels of positive affect are positively correlated with measures of extraversion and sociability, assessed from 3 months to 10
years apart (Costa & McCrae, 1980; Headey & Wearing, 1989).
For example, returning once again to the yearbook study, women
who expressed positive affect at age 21 were more likely to
describe themselves as high in affiliation many years later—at
ages 43 and 52 (Harker & Keltner, 2001).
Social Interaction and Activity
Corroborating the fairly extensive correlational results, longitudinal studies of older people have shown a significant association
between chronic, global happiness, and participation in a variety of
activities 18 months later (Kozma & Stones, 1983; Stones &
Kozma, 1986). As another example, in a recent short-term prospective study, happy students were more likely than their less
happy peers to adopt over the course of a semester a variety of new
goals and activities that “gave them a boost” (Sheldon & Lyubomirsky, in press).
Another study focused on temporary pleasant moods, rather than
long-term happiness, as a possible stimulus for engagement in
social and recreational activities (Lucas, 2001). In this investigation, positive affect at Time 1 predicted the amount of time
participants spent on recreation and on activities with friends and
family members at Time 2, even after controlling for Time 1
activity levels.
Summary
As can be seen in Panel 5 of Table 2, reasonably strong evidence
exists for the hypothesis that happiness precedes desirable resources and behaviors relevant to sociability and activity, such as
extraversion and engagement in activities. The mean effect size (r)
for these studies is .25.
Prosocial Behavior
In the only relevant longitudinal work we identified, research
suggests that altruism may follow from happiness, as well as the
reverse. Thoits and Hewitt (2001) showed that the causal connection between volunteer work and subjective well-being is bidirectional. Following a large sample over two waves, they found those
with high happiness and life satisfaction increased in the hours
they spent in volunteer activities over the course of the study.
Controlling for other variables, a conservative estimate for the
effect size (r) is .04. At the same time, those who volunteered more
hours increased in happiness.
Health Perceptions and Healthy Behavior
We identified only a few relevant longitudinal studies in this
area. A daily diary study of sickle cell disease patients found that
positive mood during Day 1 was related with lower reported pain
during Day 3 (Gil et al., 2004, cited previously). Furthermore,
happy people were less likely to drink and smoke 5 years later in
a Russian study (Graham et al., in press; see also Peterson et al.,
1998) and to describe themselves as healthy in a U.S. study
(Stones & Kozma, 1986).
Immunity
Intriguing short-term longitudinal results were obtained from a
recent study that examined immunity indirectly, by assessing susceptibility to illness (Cohen, Doyle, Turner, Alper, & Skoner,
2003). Healthy volunteers were exposed to a rhinovirus and monitored for host resistance to the common cold. Those with a
positive emotional style—that is, who typically reported experiencing positive emotions—were relatively less likely to develop a
cold, and, important to note, this association was independent of a
negative emotional style. Furthermore, typical negative emotional
835
experience was not associated with colds. Finally, in a study
assessing immune function directly, cancer survivors with more
uplifts than hassles showed enhanced NK cell activity 18 months
later (Vitaliano et al., 1998).
Coping
Research using prospective longitudinal designs has examined
coping as a function of traits linked with positive functioning, such
as optimism, as well as a function of preexisting positive mood.
For example, optimism has been associated with better psychological adjustment after breast cancer diagnosis (Epping-Jordan et al.,
1999), with the tendency to cope with breast cancer surgery
through active engagement (Carver et al., 1993) and with more
problem-focused coping and less denial following open-heart surgery (Scheier et al., 1989). Furthermore, two studies focused on
the link between positive mood and coping with potential and
actual breast cancer. C. C. Chen and colleagues (1996) found that
positive affective responses predicted engaged coping among
women who were undergoing biopsy for suspected breast cancer.
A study predicting coping and mood following surgery for breast
cancer showed that preexisting levels of positive mood predicted
the tendency to cope through active engagement (Carver et al.,
1993). The effect of positive affect on coping has also been
demonstrated in research by Fredrickson and Joiner (2002), who
found that experiences of positive emotion at one time period were
associated with more effective coping and even greater positive
experiences later.
Conclusion
In summary, accumulating research shows that happiness, pleasant moods, and closely related constructs precede indicators of
physical well-being and adaptive coping (mean r ⫽ .27; see Panel
7 of Table 2).
The vast majority of investigations in this area are experimental
and thus are described in the next section. The only longitudinal
study we identified was conducted by Staw and his colleagues
(2004), who found that positive affect expressed on the job by
employees predicted their supervisor’s evaluation of the employees’ creativity a year and a half later (r ⫽ .31).
Conclusion
The longitudinal literature is undoubtedly sparser than the crosssectional work. We were able to identify few relevant longitudinal
studies in several areas and none at all examining the topics of
perceptions of other people, enjoyment of social activities and
interactions, likability, negotiation and conflict resolution, and
performance of complex mental tasks. Despite the scarcity of
relevant studies, every single investigation we found corroborated
the correlational findings in the direction predicted by our model.
That is, both long-term happiness and short-term pleasant moods
tend to precede the desirable characteristics, resources, and behaviors with which they are correlated. Clearly, longitudinal research
should be a high priority for the future. Fortunately, many relevant
experimental studies exist, which offer an even stronger test of our
836
causal hypothesis regarding happiness and success. In the next
section, we document the fairly large and growing literature examining the effects of induced pleasant affect—the hallmark of
long-term happiness— on behaviors, resources, and skills paralleling culturally defined success. In short, this evidence addresses the
critical question of whether positive affect causes the adaptive
characteristics that help happy people thrive.
EXPERIMENTAL EVIDENCE
Question 5: Does Positive Affect Lead to Behaviors
Paralleling Success?
Positive Perceptions of Self and Others
Self-Perceptions
Experimental work on induced positive moods suggests that
positive emotions have a causal influence on positive self-feelings.
For example, students induced into experiencing a positive mood
thereafter describe themselves in more positive terms (Sarason,
Potter, & Sarason, 1986), assess their task performance as superior
(Barsade, 2002), report more favorable global self-evaluations, and
recall more positive experiences and successes from their past
(M. S. Clark & Waddell, 1983; Wright & Mischel, 1982). Happy
moods also appear to increase feelings of self-efficacy. Those put
in a good mood report higher self-efficacy (Baron, 1990; Samson
& Rachman, 1989; Schuettler & Kiviniemi, in press) and set
higher goals for themselves (Baron, 1990; Hom & Arbuckle,
1988). Induced positive moods also lead people to expect more
success on laboratory tasks (Brown, 1984; Wright & Mischel,
1982). Finally, positive mood leads individuals to attribute their
successes to stable factors within themselves (Brown, 1984). Thus,
the evidence indicates that positive affect makes people feel good
about themselves.
Perceptions, Memories, and Judgments of Others
A review of the experimental literature suggests that short-term
positive affect triggers not only positive feelings about oneself, but
also promotes greater liking and fondness for others. Experimental
studies show that individuals induced to feel happy are more likely
than those induced to feel sad to express liking for a stranger
(Baron, 1987, 1993; Griffitt, 1970). For example, in one set of
studies, students were asked to conduct a simulated job interview.
After the interview, those who had been previously induced into a
positive mood rated the applicant higher on a number of jobrelated and personal dimensions and were more likely to “hire”
him or her than those induced into a negative mood (Baron, 1987,
1993; see also Baron, Rea, & Daniels, 1992). Happy moods have
also been found to be associated with recollections of positive
information about a particular person (Baron, 1987).
In this section, we consider the literature on sociability, activity,
and energy, to determine whether happy moods prompt more
frequent, more enjoyable, and higher quality social activities and
interpersonal interactions.
Laboratory studies in which positive moods have been induced
support the causal direction from positive affect to sociability
(Isen, 1999). In seminal research in this area, Isen (1970) found
that participants induced into a positive mood were more sociable
with a confederate—for example, more likely to initiate conversation with her and be more attentive (see also McMillen, Sanders,
& Solomon, 1977). Almost 20 years later, a very similar study
found analogous results (Cunningham, 1988b). When left alone
with a female peer, male participants induced to feel happy were
more likely to engage in social interaction and self-disclosed more
to her than those induced to feel sad. Cunningham (1988b) suggested that the positive affect may have increased the men’s
feelings of energy for engaging in a social interaction and raised
their expectations for rewards from the communication. In another
study, participants who had been induced into a pleasant mood
reported that they would choose to engage in social activities if
they feel happy (Cunningham, 1988a). In summary, these studies
support the notion that positive moods have beneficial social
consequences. When feeling happy, people tend to seek out social
interactions, in part because they are likely to view such interactions as rich and rewarding (Schaller & Cialdini, 1990).
Social Interaction, Activity, and Energy
Evidence supporting the argument that positive affect promotes
activity, as opposed to the reverse, comes from a well-cited experimental study (Cunningham, 1988a). Students who were induced to feel happy, relative to those induced to feel neutral or sad,
showed greater interest in leisure activities (e.g., eating good
meals, planning a trip or vacation, or going to a party or a sporting
event, rock climbing, and shopping; Cunningham, 1988a). Thus,
one of the benefits of positive affect is that it appears to boost
activity, energy, and involvement in active leisure.
Enjoyment of Activities and Social Interactions
When experiencing a pleasant mood, people are more likely,
rather than less, to enjoy their activities and social interactions and
to derive more satisfaction from them. Laboratory studies have
shown that induced happy moods lead participants to derive
greater enjoyment from whatever task they are instructed to perform. For example, individuals in an induced positive mood are
more likely to enjoy category-sorting tasks (Hirt, Melton, McDonald, & Harackiewicz, 1996; Murray, Sujan, Hirt, & Sujan,
1990), and groups put in a positive mood are more likely to take
pleasure in a group negotiation task (Carnevale & Isen, 1986).
Summary
In summary, transient happy moods appear to lead people to
seek out others and to engage with the environment at large, to be
more venturesome, more open, and more sensitive to other individuals (Veenhoven, 1988). Supporting this thesis, temporary elation has been associated with greater perceived relationship closeness and self– other overlap (Waugh & Fredrickson, 2003; see also
Kashdan & Roberts, 2004); increased activity and physical arousal
(Schaller & Cialdini, 1990); as well as excited, affectionate, and
affiliative feelings (Lucas, 2001; Watson, 1988). Additionally,
positive mood is related to higher levels of energy (Lehr, 1982).
Watson (2000) reviewed evidence showing that both positive
affect and feeling lively and energetic are aspects of extraversion.
Indeed, feeling active is so highly correlated with positive affect
that Watson and colleagues used it as one marker for positive
emotions on their affect measure, the widely used Positive and
Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen,
1988).
Negotiation and Conflict Resolution
The experimental evidence supports the argument that pleasant
moods boost people’s abilities at resolving conflict. In one study,
those with induced positive affect showed a decreased preference
for resolving conflict through avoidance and an increased inclination for reducing conflict through collaboration (Baron, Fortin,
Frei, Hauver, & Shack, 1990). Similarly, participants with induced
positive mood were found to make more concessions during faceto-face negotiations, and to have a weaker preference for handling
future conflicts with avoidance and competition (Baron, 1990; see
also Baron et al., 1992). In the same study, those put in a positive
mood were more likely to solve conflicts through collaboration,
and were more likely to offer help to others. Finally, Forgas (1998)
found that an induced positive mood had a beneficial influence on
bargaining across a variety of negotiation tasks, with individuals in
pleasant moods revealing a marked penchant to be more cooperative and less competitive (see also Barsade, 2002). Similar results
have been obtained when investigating groups. Carnevale and Isen
(1986) found that a group put in a positive mood was more likely
to reach an optimal agreement and less likely to break off negotiation and to use aggressive tactics.
Prosocial Behavior
In reviewing the experimental research on mood and prosocial
behavior, we address the question of whether the experience of
pleasant moods stimulates people to be more altruistic, generous,
and charitable people.
Numerous experimental studies have found that happy moods
increase the likelihood and amount of helping. Indeed, this effect
is one of the most robust findings in the literature on positive mood
and social behavior, having been variously called the “feel good,
do good” phenomenon, the “glow of goodwill,” and the “warm
glow of success.” Both the inductions of positive mood and the
assessments of helping have taken numerous forms in these studies. For example, happy moods, in comparison with sad or neutral
moods, have promoted such behaviors as contributing money to
charity (Cunningham, Steinberg, & Grev, 1980; Isen, 1970) or to
needy children (Rosenhan, Underwood, & Moore, 1974), donating
blood (O’Malley & Andrews, 1983), and volunteering for an extra
experiment (Aderman, 1972; Baron & Bronfen, 1994; Baron et al.,
1992; Berkowitz, 1987; Isen & Levin, 1972; Rosenhan, Salovey,
& Hargis, 1981).
In summary, the extensive experimental evidence indicates that
positive affect fosters helping behavior. However, what about
negative affect? Of interest, negative moods such as sadness or
guilt have also been shown to promote helping—for example,
when the helping promises to improve mood (e.g., Manucia,
Baumann, & Cialdini, 1984), when the person in need calls atten-
837
tion to his or her plight (McMillen et al., 1977), when the negative
mood does not lead to self-preoccupation (Kidd & Marshall,
1982), or when individuals feel they have harmed someone
(Salovey, Mayer, & Rosenhan, 1991). Thus, researchers have
argued that positive moods lead to helping under the majority of
circumstances, whereas negative moods lead to helping only under
certain conditions—namely, when the rewards of helping are high
and the costs are low (e.g., Cunningham, Shaffer, Barbee, Wolff,
& Kelley, 1990). In contrast, there appear to be multiple reasons
that positive affect fosters helping (Carlson, Charlin, & Miller,
1988).
The research evidence consistently shows that happy moods
lead to increased helping. What are the mechanisms underlying
this effect? Considerable theoretical discussion has focused on this
question (e.g., Batson, 1990; M. S. Clark & Isen, 1982; Salovey &
Rosenhan, 1989; Schroeder, Penner, Dovidio, & Piliavin, 1995),
and a variety of hypotheses have been advanced. The most persuasive evidence supports the view that happy moods lead to
helping through increases in positive thoughts and more favorable
judgments of others—for example, by increasing liking for other
people (Baron, 1987, 1993; Griffitt, 1970) and enhancing one’s
sense of advantageous resources and good fortune that should be
shared equitably with others (Aderman, 1972). Furthermore, individuals in pleasant moods may expect that helpfulness will evoke
gratitude and appreciation—that is, they anticipate positive outcomes and rewards of helping, as opposed to the potential costs
(M. S. Clark & Waddell, 1983; Cunningham, 1988a). In addition,
people in a happy mood may be more likely to recall the positive
aspects of their past helping experiences (M. S. Clark & Isen,
1982, 1983) and to view themselves as more generous people, as
well as to feel more confident, efficacious, resource laden, in
control, and optimistic about their ability to help (M. S. Clark &
Isen, 1982; Cunningham, 1988a; Taylor & Brown, 1988).
In summary, the sizable experimental literature on helping offers persuasive evidence suggesting that positive affect heightens
generosity and helpfulness. Moreover, because this research primarily comprises studies involving experimental inductions of
mood, the causal direction is generally known. That is, although
helping undoubtedly elevates mood, we can be confident that
pleasant moods also foster helping.
Health Perceptions
Although the experimental literature in the area of health perception is scarce, the results are intriguing. For example, individuals induced into a happy mood have shown relatively higher pain
thresholds (Alden, Dale, & DeGood, 2001; Cogan, Cogan, Waltz,
& McCue, 1987) and lower blood pressure reactivity to a stressful
task (T. W. Smith, Ruiz, & Uchino, 2004). Happy moods may also
promote health by boosting self-efficacy, optimism, and efforts to
battle illness. Participants who imagined being diagnosed with
kidney cancer and then induced into a pleasant mood reported
greater ability in managing the illness, more optimism about their
prognosis, and stronger intentions to follow the treatment regimen
and exert effort to overcome the illness than those induced into a
negative mood (Schuettler & Kiviniemi, in press, also cited
earlier).
838
Healthy Behavior
An intriguing line of research suggests that positive moods
might help people exert willpower and self-control over unhealthy
or harmful urges and addictions. For example, Tice and Wallace
(2000) showed that inducing positive mood replenishes the depleted ego, allowing individuals to demonstrate will power once
again after it has been worn down by temptation. This finding is
consistent with a correlational study of smoking cessation, which
found positive affect to be negatively associated with urges to
smoke in those withdrawing from smoking (Zinser, Baker, Sherman, & Cannon, 1992).
Immunity
Research on induced mood and immune activity provides some
support for a causal relation between positive moods and immune
function. For example, in several investigations, participants who
had watched a humorous videotape showed increased levels of
immunity (Dillon et al., 1985; Lefcourt, Davidson-Katz, & Kuenemen, 1990; McClelland & Cheriff, 1997; however, see Martin,
2002, for a critique of these data). Furthermore, in a small sample
of trained actors, induced positive and negative mood were found
to be related inversely to changes in immune function (Futterman,
Kemeny, Shapiro, & Fahey, 1994). That is, level of proliferative
response to phytohaemagglutininantigen, a marker of immune
function, increased in response to positive mood and decreased in
response to negative mood (see also Solomon, Segerstrom, Grohr,
Kemeny, & Fahey, 1997).
Flexibility and Originality
Does a happy mood prompt a person to be more original and
more flexible in his or her thinking? Many investigators, led by
Isen and her colleagues, have shown that laboratory inductions of
positive affect lead to elevated scores of originality and flexibility,
often labeled creativity in these studies. The manipulations used to
induce positive moods have been diverse, and several different
types of assessments of flexible and original thinking have been
used (e.g., Estrada, Isen, & Young, 1994; Hirt et al., 1996; Isen,
Johnson, Mertz, & Robinson, 1985; Sinclair & Mark, 1995). It
should be noted that, occasionally, people in a sad mood are also
more original than those in a neutral mood (Adaman & Blaney,
1995). However, in most studies, it is the positive group that shows
the most original responses. Isen (1993) reviewed the extensive
evidence linking induced positive affect to creativity on laboratory
measures and concluded that there is little doubt that induced
positive affect, compared with neutral affect, heightens performance on the laboratory tasks.
In conclusion, the laboratory findings on induced positive
moods suggest that pleasant emotions enhance performance on
simple measures of flexible thinking and originality. What are the
mechanisms by which positive affect leads to creativity? The
association might be due to the fact that positive moods make the
person feel safe and secure, and, therefore, lead him or her to think
in more divergent ways without feeling threatened—in other
words, to be more playful (Fredrickson, 1998, 2001). Kahn and
Isen (1993) found that individuals put in a good mood sought more
variety in their choices, suggesting the role of feeling secure and
wanting to try new things as a causal mechanism for the effects of
positive affect on creativity.
Performance on Complex Mental Tasks
The data reviewed previously persuasively suggests that positive affect enhances creativity. Does positive affect also boost
performance on complex mental tasks? The experimental evidence
regarding this question is rather mixed. One body of evidence
indicates that positive affect might be detrimental to accurate
judgments and logical, rational decision making. Early research on
affect and judgment suggested that people in a positive mood
might perform more poorly, compared with those in neutral or sad
moods, at decision and judgment tasks. Those induced to feel
positive affect have been shown, for example, to do worse at logic
problems (Melton, 1995) and to be less likely to differentiate
strong from weak arguments in a counterattitudinal essay paradigm (Mackie & Worth, 1989). In contrast, individuals induced to
be in a sad mood are more likely to value the quality of arguments
over the source (Bless, Bohner, Schwarz, & Strack, 1990), less
likely to rely on stereotypes (Edwards & Weary, 1993), and more
likely to carefully execute all steps in a highly structured task
(Elsbach & Barr, 1999). Furthermore, the literature on depression
suggests that people in normal moods, as compared with dysphoric
people, are often more likely to overestimate their degree of
control (Alloy & Abramson, 1979). However, it should be noted
that tests of the depressive realism hypothesis have yielded mixed
results, with about as many studies inconsistent with the idea as
there are supporting it, depending on the outcome measured (e.g.,
Dobson & Pusch, 1995; see Ackermann & DeRubeis, 1991, for a
review).
Other studies, however, have shown that those put in a pleasant
mood outperform others. For example, people in a positive mood
made faster and more efficient decisions regarding a personally
relevant task (Forgas, 1989), performed better on a clerical errorchecking task (Jundt & Hinsz, 2001), solved more anagrams
correctly (Erez & Isen, 2002), and considered the correct diagnosis
of a disease earlier (Estrada, Isen, & Young, 1997). The results of
other studies also suggested more efficient processing—that is,
participants placed in a positive mood were less likely to review
information they had already seen, were more likely to ignore
information judged as unimportant (Isen & Means, 1983) and to
adopt an efficient strategy for performing a clerical coding task
(Baron, 1990), and showed less anchoring when making a diagnosis (Estrada et al., 1997). In addition, the respondents high in
positive affect tended to eliminate alternatives that did not meet a
minimum cutoff on important dimensions, a more efficient strategy. People in induced happy moods also appear to persist longer
at tasks in which perseverance is required (Erez & Isen, 2002;
Kavanagh, 1987). Finally, people in a good mood have been
shown to make more inclusive group representations, defining
others as part of their in-group (Dovidio, Gaertner, Isen, & Lowrance, 1995; Isen & Daubman, 1984) and using a broader definition of group membership (Urada & Miller, 2000). Thus, individuals in a happy mood are more likely to use stereotypes, but they
are also more likely to include more diverse people within their
groups.
Complicating the picture, another body of evidence suggests
that, although individuals in a sad mood often perform well at
simple analytical tasks, they are not immune to errors. For example, sadness can open the decision maker to greater anchoring
effects (Bodenhausen, Gabriel, & Lineberger, 2000), because
greater analytical processing can make the anchor more salient.
Indeed, Staw and Barsade (1993) concluded that the evidence on
decision making and mood supports the happier-and-smarter effect
rather than the sadder-but-wiser hypothesis.
Heuristic versus analytical processing. Based on the research
reviewed previously, it appears that sometimes the performance of
people in positive moods is superior, sometimes equal to, and at
other times inferior to mildly depressed people and those in a
negative mood. How can we make sense of this seemingly contradictory evidence? One conclusion is that the experience of
positive affect is not beneficial—and perhaps harmful—when one
is engaged in mental tasks. Another response to the mixed evidence is to try to determine in which situations people in a happy
mood do better at judgment and decision problems and in which
situations they do worse. In recent years, a perspective has
emerged that people in positive moods interpret their affect as
signifying that events are going well. Thus, they are quicker to
make decisions and are likely to use general heuristic answers
learned in the past. After all, if all is well, then past successful
answers are likely to work. Thus, the person in a positive mood is
likely to rely on preexisting general knowledge structures (Bless et
al., 1996) that have previously succeeded, because the situation is
seen as predictable and safe. In contrast, people in negative moods
are likely to process problems analytically and vigilantly.
Heuristic shortcuts can be likened to mental habits, which allow
for less effortful processing. Because heuristic answers are efficient when they are appropriate to the task, people in happy moods
can solve complex tasks better and faster, thus freeing cognitive
capacity for other challenges. At the same time, when researchers
present respondents with analytical tasks for which past heuristics
are not suitable, the performance of sad participants is likely to be
superior, especially when no task performance feedback is involved. Because sad individuals are more likely to use effortful
and detailed processing for every task, they will do better at
problems for which past learning is not particularly helpful. However, the effortful processing of sad people will not be desirable for
complex tasks on which efficient strategies learned in the past can
be used. For instance, Isen and Means (1983) found that people
induced to feel happy were better at performing a complex mental
task—namely, eliminating unimportant information and discovering useful heuristics to help solve a difficult problem. Sad individuals, in contrast, are more likely to ignore heuristic shortcuts
and to use effortful, vigilant processing even when it is not
required, and therefore perform poorly in complex and timelimited situations (Gleicher & Weary, 1991). For example, in a
recent study, sad participants displayed reduced accuracy of social
judgments based on thin slices of nonverbal behavior (Ambady &
Gray, 2002). The authors showed that sadness impaired accuracy
by promoting a deliberative style of processing information, which
can interfere with a person’s ability to understand others.
Notably, happy moods can produce good performance even on
complex and novel tasks, but only when cues are present to
indicate that the situation is important and that care is required.
That is, because positive affect signals that all is well in the
839
situation, when novel problems are encountered, individuals in a
pleasant mood require information indicating that they need to
exert additional effort, to consider new and careful strategies, and
not to rely on preexisting mental structures. As Schwarz (1990)
noted, people in good moods see little need to expend effort unless
it is necessitated by currently active goals. However, cues about
motivation, an explicit goal to be accurate, or the negative affect
that is engendered by initial failures at the task can all motivate the
happy person to engage in more effortful processing when it is
needed. Thus, when the situation calls for it, a person in a positive
mood is capable of slowing down and analytically thinking
through a problem in a more careful and deliberate way. For
example, people placed in a positive mood overcame their stereotypes if they learned they would be held accountable for their
decisions (Bodenhausen, Kramer, & Süsser, 1994).
A number of studies support this argument. For example, research participants who are put into a good mood seem to make
riskier judgments if nothing is at stake, but make more conservative bets when real losses are possible (see Isen, 2000, for a
review). Aspinwall (1998) reviewed evidence suggesting that people in a positive mood do not ignore negative information if it is
important and self-relevant. She hypothesized that happy people
will use heuristics in many laboratory tasks that appear to be
unimportant, but will use more systematic processing when the
task is important and self-relevant (e.g., Forgas, 1989). For example, those put in a positive mood were less likely than those in a
control condition to ignore self-relevant medical information about
their risky health behaviors, and were better able to recall the risk
information at follow-up (Reed & Aspinwall, 1998). In another
study involving an important, self-relevant situation, students who
had undergone a positive experience were more open to receiving
feedback regarding their liabilities in reaching their life goals and
their careers, whereas those undergoing a negative experience
showed greater interest in hearing about their assets (Trope &
Pomerantz, 1998). These results indicate that, in a situation in
which people’s weaknesses are related to important life goals,
having a positive experience can lower ego defensiveness and
make people more open to learning about their weaknesses.
Further support of the notion that happy moods can instigate
careful processing, but only when appropriate cues are present,
comes from research showing that people in a good mood will do
better if the task is more ecologically valid. For example, in one
study, depressed respondents made relatively less realistic predictions about future events in their own lives in the upcoming
semester (Dunning & Story, 1991). That is, depressed participants
were less accurate in their predictions, and more overconfident
than nondepressed individuals, when predictions were of future
events in their own lives rather than predictions of laboratory
stimuli (for mixed evidence, however, see Shrauger, Mariano, &
Walter, 1998). Supporting the argument that positive affect is
likely to be helpful in natural settings, several researchers have
concluded that depressive realism is found in trivial, artificial
laboratory tasks (Dobson & Franche, 1989; Pacini, Muir, & Epstein, 1998). The authors argued that dysphoric individuals use
excessive rational control even in trivial situations, leading to their
superior performance in certain inconsequential laboratory tasks,
but that nondysphoric individuals perform more optimally in consequential contexts. Corroborating this conclusion, people in an
induced positive mood were found to take more risks when the
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stakes were low, but to be more risk averse when the stakes were
high (Isen & Geva, 1987; for similar findings, see Isen & Patrick,
1983; Leith & Baumeister, 1996).
Summary. In conclusion, happy moods are not a panacea when
it comes to solving problems or tackling laboratory tasks. Happy
people’s reliance on simple heuristics is a handicap if they are
attempting a novel task for which previous knowledge is not
useful. On the other hand, their more frequent use of mental
shortcuts allows them to allocate additional resources to secondary
tasks, thus, permitting them to use their time and resources more
efficiently and to perform well at complex problems (Isen, 2000).
Furthermore, people in a positive mood are more likely to have
richer associations within existing knowledge structures, and thus
are likely to be more flexible and original. Those in a good mood
will excel either when the task is complex and past learning can be
used in a heuristic way to more efficiently solve the task or when
creativity and flexibility are required. However, many laboratory
activities provide little or no feedback to participants on how they
are performing on the task, and, thus, the happy person has no clue
in those studies that things are going poorly. In contrast, research
suggests that, in everyday, naturalistic situations, a person in a
happy mood will quit relying on heuristics when he or she sees that
they are not working. In summary, the evidence shows that people
experiencing happy moods have potential deficits when it comes to
problem solving, but they can overcome these deficits if they are
motivated to perform well at the task.
CONCLUSION
To sum up then, we return to our initial question: Does positive
affect promote positive, favorable characteristics? Our review of
the relevant experimental literature reveals compelling evidence
that positive affect fosters the following resources, skills, and
behaviors: sociability and activity (mean r ⫽ .51), altruism (mean
r ⫽ .43), liking of self and others (mean r ⫽ .36), strong bodies
and immune systems (mean r ⫽ .38), and effective conflict resolution skills (mean r ⫽ .33). The evidence is weaker, but still
consistent, that pleasant moods promote original thinking (mean
r ⫽ .25). It is fair to say that the evidence is almost nonexistent
regarding whether individuals induced to experience happy moods
also have superior coping abilities, greater popularity, and healthier behavior. Finally, positive affect sometimes leads to poor
problem solving and sometimes to more efficient solving of complex tasks, depending on the situation. It is intriguing, however,
that despite the presence of some contradictory results, the mean
effect size for performance on complex mental tasks is .25.
Summarizing the Evidence
In this article, we set out to document three classes of evidence
to test our conceptual model, positing that happiness, or the longterm propensity to experience frequent positive emotions, promotes culturally valued success and thriving (see Figure 1). To this
end, we documented the cross-sectional, longitudinal, and experimental literature examining happiness and positive affect and
their associations with successful outcomes, as well as with behaviors paralleling success. Although our review revealed gaps in
the existing research, it also highlighted the robustness and wide
range of the relationships that were observed. First, as indicated by
the consistently strong average effect sizes, we discovered a vast
number of correlational studies showing positive associations between happiness and successful outcomes within all of the major
life domains (i.e., work, love, health). Second, cross-sectional
work indicated copious positive relations of happiness and positive
affect with an array of desirable attributes, propensities, and behaviors (e.g., positive perceptions of self and other, sociability,
prosocial behavior, likability, creativity, and coping, among
others).
Third, although the longitudinal literature was found to be quite
limited— especially when compared with the cross-sectional body
of evidence—it was persuasive in showing that many of the
correlations we had documented were replicated within the temporal sequence predicted by our model. That is, a number of
studies demonstrated that (a) long-term happiness precedes the
successful outcomes with which it correlates and (b) both longterm happiness and short-term positive affect precede the desirable
resources and characteristics with which they are related.
Finally, and perhaps most important, a sizable experimental
literature offered strong evidence that short-term positive affect—
the hallmark of a happy person— causes a range of behaviors
paralleling success. These data suggest that positive affect may
very well be the critical mediator underlying the relationship
between happiness and culturally valued success. In summary,
although many researchers presuppose that happiness follows from
successes and accomplishments in life, our review provides strong,
albeit not conclusive, evidence that happiness may, in many cases,
lead to successful outcomes, rather than merely following from
them.
Questions, Caveats, and Future Research
The evidence presented here highlights the functional benefits
of positive affect and chronic happiness. It would be absurd,
however, to suggest that chronic happiness is necessary for all
forms of success and thriving. Plenty of exceptions are in evidence. The conclusion we draw is much more modest—that positive affect is one strength among several that can help achieve
approach-oriented success. Certainly other resources, such as intelligence, family connections, expert skill, and physical fitness,
can also figure prominently in success. In this section, we discuss
questions arising from our preliminary conceptual framework,
bring up several potential limitations and empirical issues, and
outline the important empirical research that is needed to address
the remaining issues.
Methodological and Generalizability Issues
Experimental and Longitudinal Designs
More experimental and longitudinal research is needed in a
number of areas to fill the gaps in our review and to provide a
stronger test of our conceptual model. For example, although a
substantial amount of experimental research has been conducted in
the areas of creativity and altruism, less experimentation exists on
the effects of positive affect on likability, coping, and health.
Furthermore, few or no longitudinal studies exist in many areas,
including friendship, judgments of others, organizational citizenship, negotiation, and performance of mental tasks. Longitudinal
research is also essential to confirm that positive affect predicts
success even after controlling for earlier levels of resources and
success. Cross-sectional studies, in which outcomes are simply
correlated with happiness, might produce spurious findings that are
due to the causality running from the variable to happinessor the
presence of third variables. For this reason, it is important to
consider longitudinal studies that examine the effects of happiness
on outcomes while controlling for happiness at Time 1 or for
potential third variables at Time 1. For example, baseline health
might be controlled in a study of the effects of happiness on
longevity. Although this procedure has not always been followed
in the studies we review, and represents a serious limitation of
some investigations, it should be a high priority for future research.
Finally, the existing evidence for a causal link between positive
affect and behaviors that lead to success currently comes from
short-term laboratory studies (Type C evidence in Figure 1). If
these same behaviors are also increased by long-term interventions
to enhance global happiness and happy moods (e.g., through
self-help, therapy, or drug treatment)—that is, collect evidence of
Type D—the case for happiness being causally related to success
will be strengthened even more.
Reporting Biases
A good portion of the evidence presented in this article involves
self-reports, which are prone to the biasing effects of mood. That
is, an individual in a happy mood is likely to rate everything as
positive, including herself, her health, her job, her coping abilities,
and her marriage. Such potential reporting biases may render some
of the reviewed findings to some extent ambiguous. This is undoubtedly a limitation of the evidence. Fortunately, however,
many of the outcomes we reviewed are not dependent on global
self-reports. Among others, these include mortality data, immune
marker levels, income, job supervisor ratings, peer judgments,
marital status, and laboratory measures of creativity, task performance, and helping. Furthermore, positive mood biases may sometimes not be artifactual, but, instead, may represent the phenomenon in question. Clearly, more non–self-report measures of key
variables are needed in future studies.
Assessing Happiness and Positive Affect
Can we be certain of the validity of the happiness measures used
in the literature reviewed here? Recall that our conceptual model
rests on a definition of happiness as the frequent experience of
positive emotions. In the studies we review, some of the long-term
measures assess happiness, others more directly assess the longterm experience of positive affect, and yet others assess what
Veenhoven (1984) labeled the “hedonic level of affect” and what
Bradburn (1969) called “affect balance”—the experience of positive emotions minus negative emotions. Regarding measures of
happiness, Diener and his colleagues (1991) reported that individuals scoring high on such scales do indeed experience frequent
positive emotions. Studies using hedonic-level-of-affect measures
also provide a test of our model—albeit a less direct one— because
positive affect represents half of such scores. Although negative
affect is also included in affect balance measures, the outcomes we
review (such as sociability) are likely to result from positive affect.
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What about measures of positive affect such as the PANAS
(Watson et al., 1988), whose items (e.g., excited, active) might be
the ingredients leading to success rather than pleasant emotions per
se? Although an important topic for future research is dissecting
the various types and components of positive emotions, there is
reason to believe that positive emotions, not merely its components
like energy or arousal, play a causal role. First, experimental
manipulations of positive affect produce similar outcomes, and
many of these manipulations (e.g., receiving a small gift or listening to soothing music) are unlikely to produce high levels of
arousal or activation. Second, measures of less activated forms of
positive emotion such as happy or content produce effects that are
consistent with those found with the whole PANAS. Thus, positive
emotions do appear to be an active ingredient leading to successful
outcomes, although researchers need to explore further the augmenting role of arousal/energy and the possibly diverging role of
various specific types of positive emotions. For example, high- and
low-arousal positive emotions may activate different types of goals
and behaviors, such as the goals of influencing versus adjusting to
one’s environment, respectively (Tsai, Knutson, & Fung, 2004).
Alternative measures of positive affect that have been included
in studies reviewed here include facial expressions of positive
emotion and positive emotional language usage. Research using
such measures is not susceptible to self-report biases and is relatively free from the conceptual ambiguities that characterize our
current understanding of the structure of positive emotion. That
studies using these more subtle measures have supported the
relations of positive affect to valued life outcomes (and have
typically done so longitudinally) strengthens the case for the
contribution of positive emotional experience in securing the
goods of life.
Generalizability
A critical question is the degree to which the findings presented
in this article generalize to other cultures. The majority of the
evidence we reviewed comes from Westernized, industrialized
nations, where positive emotions are highly valued (e.g., Eid &
Diener, 2001). Other cultures, by contrast, give less emphasis to
happiness and its pursuit (Lyubomirsky, 1997; Suh, 2000) and
hold different definitions of success. It is possible then that the
characteristics that follow from positive emotions, such as selfconfidence, activity, sociability, and original thought, are more
likely to lead to success in some societies than in others. For
example, cultures centered around the idea of avoiding bad outcomes rather than approaching good ones may be less likely to
reward those who are high in positive affect. Happiness might lead
to outcomes that are considered beneficial only in particular types
of societies that happen to be where most research to date has been
conducted. Alternatively, the relation between positive affect and
success behaviors may also be universal across cultures or may be
nearly universal in resource-rich societies. Furthermore, perhaps
all cultures value the three domains of success we posit—work
life, social relationships, and health— but they prioritize them
differently. Thus, an important objective for future research is to
collect cross-cultural data so that the limiting conditions of our
framework can be understood.
842
Situational Specificity
Although we found consistent effects for happiness across all
three classes of evidence, there was significant heterogeneity
across studies in a number of areas, suggesting the need for future
research to explore moderators of the effects of happiness and
positive affect. For example, although Diener and colleagues
(2002) found that happier college students earned more income
many years after leaving college, this effect was moderated by
parental wealth, with richer students benefiting more from being
happy. Furthermore, there are times when being flexible, sociable,
and optimistic might not be appropriate, and might distract one
from the task at hand. For example, some clerical or accounting
jobs might require a person who is asocial and quiet as well as very
careful about errors and who strictly follows rules. Lucas and
Diener (2003) discussed the types of jobs that might most profit
from happy workers and those vocations in which happiness might
be less of an advantage.
In summary, perhaps the most important limitation to our conclusions is that being happy is more adaptive in certain situations
than in others. For example, happiness may be most functional
when it occurs in benign life circumstances and may be less
helpful during dangerous times. Nonetheless, the advantages of
happiness reviewed in this article are rather striking, and a major
task for future research will be to determine whether these benefits
always accrue or whether circumstances exist in which a dysphoric
personality leads to greater success.
Causality and Possible Third Variables
We found in our review that experimental studies generate the
same basic conclusions as the cross-sectional and longitudinal
research. Yet, a question nags: Could happy people be successful
simply because they possess more resources in the first place,
which is the reason they are happy? Certainly, some of the effects
we review may be due to some degree to the effects of beneficial
circumstances on happiness (e.g., Headey & Veenhoven, 1989).
The longitudinal research, however, suggests that this is not the
complete explanation because happiness often long precedes the
successful outcomes. For example, happiness in college (long
before the person enters marriage or the workforce) precedes
higher income and a more satisfying marriage many years later,
even when Time 1 factors are controlled. Furthermore, in an
18-month longitudinal study that used causal modeling to test two
competing models—that is, happiness as influencing five of its
correlates versus the reverse—the results supported the happinessas-cause model for 17 out of 18 predictions that differentiated the
models (Stones & Kozma, 1986). Nevertheless, the possibility
remains that individuals with certain personal resources such as
good social skills, high activity levels, self-efficacy, and creativity
are likely to be more successful at an early age, and, hence, to
maintain and reinforce their success and happiness at a later age
because they continue to have more personal resources and, therefore, more successes. Again, however, the laboratory experimental
studies suggest that this is not the entire explanation for happy
people’s success.
Furthermore, it is important to establish that positive affect is the
key variable associated with desirable characteristics and, ultimately, with successful outcomes—not the absence of negative
affect or depression. Some studies are able to address this question
directly, as they include both positive affect and negative affect as
variables or manipulations. For example, in the helping literature,
experimental inductions of positive affect produce increased
prosocial behavior, whereas inductions of negative affect do not
necessarily decrease helping (e.g., Aderman, 1972; Berkowitz,
1987; Isen & Levin, 1972; Rosenhan et al., 1974). Regarding
social interactions (e.g., Cunningham, 1988a, 1988b), creativity
(e.g., Hirt et al., 1996; Richards, 1994), and evaluations of strangers (e.g., Baron, 1987, 1993), it appears that positive mood inductions do not produce symmetrical effects. Experimental laboratory
research—for example, on helping, creativity, and task performance—also often includes neutral mood conditions, which do not
parallel the effects of happy mood (e.g., Berkowitz, 1987; Cunningham, 1988a; Estrada et al., 1994; Isen, 1970, 1993; Isen et al.,
1985; Rosenhan et al., 1974). As another example, some crosssectional and longitudinal studies show significant effects of positive affect, but not negative affect—for example, on mortality risk
in the nun study (Danner et al., 2001), on likelihood of developing
a cold (Cohen et al., 2003), or on organizational citizenship (Credé
et al., 2005). In summary, many of the effects we describe in this
article cannot be attributed simply to the absence of negative affect
in happy people, although it is possible that some of the effects are
due to lack of negative affect. Disentangling the effects of positive
affect from lack of negative affect or depression should be a goal
of future research, as many studies, such as in the areas of immunity and health, still consistently fail to include measures of positive emotion.
Further evidence that the effects reviewed here are not simply
due to unspecified third variables comes from within-person studies, which show that positive moods correlate over time with
desirable outcome variables. On the days or moments when people
are in positive mood states, they are more likely to feel and behave
in certain ways than when they are in negative mood states.
Although studies of within-person patterns are not conclusive
proof of causality, they add yet another type of evidence from
which the effects of positive emotions can be inferred. In summary, taken together, a variety of different sources of evidence
suggest that positive affect leads to certain outcomes rather than
simply being caused by them. Nonetheless, longitudinal and longterm experimental studies, which assess a variety of personality
characteristics, as well as positive emotions and outcomes, would
advance understanding of the intricate relations between personal
attributes and chronic predispositions to positive affect.
Process Issues
Mediation
One conclusion from our review of the empirical evidence is
that most of the effects of being happy are due to the fact that
happy people experience positive moods most of the time and, by
definition, experience them more than unhappy people. However,
happy people might possess certain characteristics and behavioral
propensities that are helpful even in the absence of a positive
mood. Although little is known at present about the genetic predispositions characterizing chronically happy individuals, we cannot discount the possibility that long-term happiness may be directly linked—perhaps through inborn correlates—with particular
desirable outcomes or characteristics (e.g., extraversion, optimism,
sociability). As a result, these qualities may be evident and may
produce beneficial results even when chronically happy people are
in neutral or even unpleasant moods.
An alternative and persuasive perspective of the role of positive
affect as mediator of the happiness–success relation comes from
Fredrickson’s (1998, 2001) broaden-and-build model. She suggested that positive emotions broaden our cognitive and behavioral
repertoire and allow for an accrual of resources, the learning of
new skills, and so forth. In our framework, the experience of
positive affect is critical—that is, individual differences in longterm happiness may influence the ease with which a person enters
a “good mood” (cf. Larsen & Ketelaar, 1991), but the affect itself
is the key to the positive outcomes associated with positive moods.
Thus, while happy people overall may be found to enjoy a variety
of positive outcomes, the frequent experience of positive mood (in
the absence of a temperamental disposition toward such moods)
should still convey benefits. Notably, because the key to success is
happy affect and not necessarily a happy genetic predisposition,
we submit that chronically unhappy people are therefore not fated
to failure. Another implication is that future happiness-increasing
interventions should focus on how people can adopt new practices
and habits, and restructure their lives, in ways that allow for a
stream of positive experiences and positive emotions (Lyubomirsky, Sheldon, & Schkade, 2005).
We know from the experimental studies that momentary positive emotions produce many of the outcomes we reviewed. We
also know from cross-sectional and longitudinal studies that happy
people exhibit behaviors that are parallel to the outcomes found in
the experimental studies. What we do not know, because there is
little research on mediation in this area, is the degree to which
people’s current moods mediate the effects of chronic happiness
on behavior. As mentioned previously, Fredrickson’s (1998, 2001)
model suggests that even in the absence of a positive mood—
indeed, even in a negative mood— happy people will generally
perform better on many tasks because of the skills they have
learned and resources they have accumulated because of their
frequent experiences of positive moods in the past (Fredrickson &
Joiner, 2002; Fredrickson et al., 2003). We suspect that this is a
case of partial mediation—namely, that happy people perform
many of the desirable behaviors we review because they are more
often in a pleasant mood, but that being happy in the past might
lead to the accumulation of skills, social support, other resources,
and adaptive habits that the happy person can use even when in a
negative mood. Thus, an individual’s current mood is likely to
produce certain benefits, but current mood might not entirely
explain the successful performance of happy people. It will be
important in future research to separate the effects of happy
temperament, happiness engendered by current life circumstances,
and induced happy moods on the characteristics we reviewed
earlier. Tests of the mediation hypothesis promise to be an exciting
direction for future scientific work.
The search for mediational variables might begin with an examination of the various outcomes we have examined here. In this
review, we have treated a variety of desirable life outcomes as if
they hold equal status in their overall importance in people’s lives.
It may be, of course, that these outcomes are themselves intercorrelated and perhaps even differentially important. For instance, the
social benefits of positive affect may be the central mediator of the
843
effect of positive affect on other life outcomes. Research that
combines a variety of life outcomes will be needed in order to
answer the important question of how these various “goods of life”
relate to and potentially promote each other.
The Varieties of Positive Affect
An important topic of investigation for the future involves
distinguishing the effects of positive affect at a general level versus
the effects of discrete positive emotions such as contentment,
affection, curiosity, elevation, pride, and joy. Specific emotions
may be linked with specific beneficial outcomes—for example,
contentment with originality, affection with sociability, pride with
helping, and curiosity with learning and problem solving—and
these possibilities remain an intriguing direction for future
research.
One important question concerns whether the effects of positive
affect that we review apply to all positive emotions or only to those
high in arousal. In the case of positive emotions, we can ask
whether the outcomes reviewed in this article apply to contentmentor only to elation and joy. The work of Watson (2000)
suggested that feelings of energy and activity are much more likely
to accompany elation than they are to accompany contentment.
However, little more is known about the effects of the two types of
affect, and whether individuals who have chronic tendencies to
contentment will be as successful as those who are prone to joy.
An interesting finding in this regard comes from a study that
predicted work outcomes at age 26 from reports of emotions at age
18 (Roberts et al., 2003). The authors found that occupational
attainment was predicted by both positive affect– communion and
by positive affect–agency, whereas financial independence was
significantly predicted only by positive affect– communion. In the
affect literature, the distinction between moods and emotions is
often seen as pivotal. However, in the literatures that we reviewed,
the two are rarely, if ever, separately measured. Thus, we are
unable to draw conclusions about whether a propensity to positive
moods versus emotions is more conducive to the outcomes we
describe. For example, the possibility exists that the types of
emotions induced in the experimental studies stand out as figure
against ground and are more likely to produce the effects outlined
in this article. On the other hand, moods are relatively longer
lasting and are less likely to be in focal awareness; therefore,
relative to emotions, moods may influence behavior in more subtle
ways. Once again, assessing moods versus emotions and examining their discrete effects on the behaviors we review, and on
various types of success, is an important avenue for future
research.
Future Research Questions
Additional questions for research are needed to extend the
pattern of findings we describe here and to support the unifying
framework we present. Because positive affect has often been
treated as an outcome rather than a predictor of the goods of life,
the potential benefits of positive affect, itself, have remained
largely untested. Hence, research should begin to address the
potential causal role of positive affect in securing positive life
outcomes. In addition, examining positive affect in this way opens
a variety of new questions for research on the positive benefits of
844
positive affect. For example, what types of success are most
enhanced by positive affect? Are there long-term beneficial outcomes in some areas for unhappy people? Are different outcomes
likely for individuals who are prone to experience different forms
of positive affect, such as joy, affection, or contentment? What are
the control processes that prevent positive affect from amplifying
in a cycle with success and moving ever upward to dysfunctional
levels? At the broadest level, what is the optimal level of positive
affect in different tasks, contexts, and cultures? Our hope is that
our review stimulates research that examines each of these questions in detail.
Is Positive Affect a Magic Elixir?
Readers of our review might conclude that happiness and positive affect are the royal road to the perfect life. Leaving this
impression is not our intent. There are a number of ways that
positive emotions can produce adverse effects. For one thing, in
some situations, positive affect is not the most functional response.
For another thing, happy people might use their creativity, selfconfidence, negotiation ability, and sociability to achieve aims that
are not beneficial to society, such as being the “king” of the local
bar or even to achieve aims that are harmful to society, such as
being an effective confidence man. It follows from our review, for
instance, that a happy member of the Mafia might be more effective than an unhappy one, and a happy scam artist might be more
effective at committing fraud without being caught. Thus, success
must be defined in terms of a cultural and behavioral context, and
positive emotions might not lead every individual to be successful
at goals that the broader culture or the world believes are desirable.
We are aware that many mildly dysphoric individuals function
very well in society. They write newspaper columns, run psychology departments, act in popular movies, argue before the U.S.
Supreme Court, and perform many other jobs with distinction.
Qualities such as intelligence, perseverance, conscientiousness,
and social criticism of the status quo are desirable characteristics in
many situations, and are to some degree separable from positive
affectivity. A happy person with these characteristics might be
very effective in many vocations and roles, but some situations
might exist, as yet undefined, in which mild dysphoria leads to
superior functioning.
It is important to keep in mind that human emotional life is rich,
and that the relations of positive affect and negative affect to
functioning are complex ones. At times, happiness will be most
adaptive and at other times may require a level of misery or at least
discontent. Our framework suggests that positive bias in a flexible
system may well be adaptive, but an exclusively happy life is not
only unrealistic—it is not necessarily the most desirable life.
A commonly expressed claim is that happy people are satisfied
with the status quo and are not motivated to accomplish new goals
or to “change the world.” The data reviewed in this article indicate
that this blanket condemnation of happiness is inaccurate— happy
people perform well in many areas of life, including domains such
as work and income, that require motivation and persistence.
Happy people can have lofty goals and experience positive affect
that is due to the progress they make toward those goals. At the
same time, specific dissatisfactions can also motivate happy people
to work for change and to pursue new directions. Andrews and
Withey (1976) found that people who are happy with their lives are
not inevitably satisfied when they judge the conditions in society;
they found that satisfaction with one’s life and with society and
government were distinct. Furthermore, many of the characteristics observed in happy people— optimism, energy, social engagement, originality, altruism, likability, productivity, good health—
are the very characteristics that could help them improve the
conditions of theirs and others’ lives.
In summary, although happy people are generally more satisfied
people, this does not appear to prevent them from being achievement oriented. Indeed, happy people appear to be relatively more
likely to seek approach goals. The interaction of cognitive judgments about the desire for change with the propensity for positive
affect is a ripe area of inquiry for the future.
Do Happy People Experience Negative Emotions?
If happy people were unable to experience negative emotions
when things go wrong, their responses would likely be dysfunctional because they might not react appropriately to threats, losses,
and other significant negative events. If, on the other hand, happy
people can occasionally experience negative emotions, as we suggest they do, they might be particularly successful because they
can be approach focused most of the time, but not when conditions
become adverse. Thus, happy people can remain in a positive
mood as long as things are positive or neutral, but experience a
negative mood when things are very bad, thus motivating them to
withdraw, conserve resources, or otherwise avoid harm. Furthermore, there may be a resetting point around a person’s current
circumstances so that people are most likely to react with negative
affect when conditions worsen from their current state (Kahneman
& Tversky, 1979).
The negative affect produced by bad events causes a change in
thoughts, feelings, and behaviors. When adverse events occur,
people quit using heuristic processing and switch to vigilant and
careful analytical processing, which does not involve an overreliance on previous solutions to challenges. The person needs to
safeguard resources and focus on solving the immediate problem.
At the same time, it is functional for the person to return to a
positive state when conditions improve. Extending the ideas of
Fleeson (2001), happiness could be considered a “density function” in which people vary in terms of how much they are in the
state of positive experience. Although frequent experience of this
state appears to be functional, an ability to react to significant
negative events is also likely to be beneficial. Thus, even happy
people show substantial variations in their moods over time. Research confirms that the happiest individuals do feel occasionally
unhappy. For example, Diener and Seligman’s (2002) happiest
college students all displayed ups and downs in their moods, but
stayed in the positive zone the majority of the time. However, the
findings showed that they also occasionally descended into the
negative range and experienced unpleasant emotions, indicating
that the happiest individuals are not trapped in a positive mood
regardless of circumstance. We found the same pattern in the
World Value Survey (1994)—most of those responding with a 10
on a 10-point life satisfaction scale (the top 14% of respondents)
reported having experienced a negative mood or emotion during
the past few weeks.
In a laboratory study, chronically happy people reported negative feelings (such as being sad, anxious, and discouraged) in
response to negative feedback about their own performance (Lyubomirsky & Ross, 1997). The fact that happy people can and do
respond emotionally to events may provide part of the answer to
why they are likely to succeed in life. Unlike in short-term mood
manipulation studies, in which an induced positive mood often
elicits an inappropriate response to the situation, in everyday life,
happy people can react negatively when it is appropriate to the
context, but later return to a positive state.
Costs and Trade-Offs of Happiness
Despite the many benefits of long-term positive affect described
in this article, happiness can have costs or downsides in some
situations and, in other situations, have trade-offs with other desired values. Happy people are characterized by certain behaviors,
as reviewed previously, and, in some situations, these characteristics may prove detrimental. For example, if a happy individual
uses a heuristic to solve a frequently encountered problem, but the
heuristic now provides the wrong answer and there is no failure
feedback available in the situation, he or she will perform more
poorly than an unhappy person. As discussed earlier, this possibility likely accounts for findings of “depressive realism” in some
studies (Alloy & Abramson, 1979), as well as for the findings that,
under some conditions, happy people show more stereotypical
thinking because they rely on heuristic shortcuts (e.g., Edwards &
Weary, 1993). To the extent that relying on shortcuts leads to
nonoptimal performance, individuals in a positive mood may suffer. Clearly, the social costs of relying on stereotypes to make
decisions may be great.
In addition, because positive moods signal to the individual that
things are going well, less happy individuals may be superior at
critical thinking and error checking (e.g., Mackie & Worth, 1989;
Melton, 1995). Thus, it is unlikely that one particular mood state
leads to superior problem solving or task performance in general—
rather, positive affect and negative affect probably have differing
effects on cognitive processing that may or may not be well suited
to a particular task. Lucas and Diener (2003) suggested that mildly
dysphoric individuals are likely to underperform in leadership and
social tasks, but might excel in jobs such as monitoring a nuclear
power plant where constant vigilance for possible problems is
absolutely essential. Clearly, the relation of moods to decision
making and problem solving is complex and the match between
individual differences and situational requirements may determine
the adaptive significance of any emotional style.
Earlier we suggested that happy people may feel positive emotions more frequently because they are relatively more sensitive to
rewards in their environment. Tying positive emotionality to
Gray’s (1994) “behavioral approach system” makes a good deal of
sense and it also highlights potential pitfalls of happiness. Clearly,
the complexity of human life requires that one avoid some circumstances, and always moving toward evermore tantalizing rewards might lead an individual’s existence to devolve into hedonism or inappropriate risk taking. In addition, the tendency to
recognize and move toward rewards in the environment may make
a happy person susceptible to approach–approach conflicts, caught
between two potentially positive life paths.
In addition to the possibility that happy individuals might be
outperformed by their less happy peers in some situations, it is
worth noting that people make trade-offs in their decisions regard-
845
ing activities and happiness. For example, compared with European Americans, Asians and Asian Americans are more likely to
persist at a task at which they are not performing well in order to
master it and may thus suffer in terms of mood because they
continue working on something at which they are not proficient
(Heine et al., 2001; Oishi & Diener, 2003). Other researchers have
found that placing high importance on goals can heighten happiness when goals are achieved, but can also increase worry about
the goals and amplify greater negative affect when the goals are
not achieved (Diener, Colvin, Pavot, & Allman, 1991; Pomerantz,
Saxon, & Oishi, 2000). Happiness is one of life’s goods, but it
exists in the context of a variety of other goods (Ryff, 1989; Ryff
& Singer, 1998). Thus, individuals might well sacrifice happiness
in the pursuit of other valued ends. Notably, however, it may be the
happy who are particularly willing to sacrifice positive affect for
the sake of other goals, primarily because of the other resources
and capacities that have been afforded to them by the experience
of happiness.
Furthermore, particular circumstances or degrees of positive
affect may lead to decrements in performance. Recent work has
shown that extremely happy individuals perform slightly lower in
some achievement situations compared with very happy persons,
suggesting that the optimum level of happiness might vary depending on the task at hand (Diener, Oishi, & Lucas, 2005).
Extremely happy individuals might be inclined to be too sociable
to perform at the maximum level in some achievement situations
that occur over long periods of time.
Very happy individuals may also be judged harshly for their
apparent satisfaction—for example, as shallow or complacent.
Indeed, research has shown that happiness in the context of a
potentially lazy or meaningless life is judged quite negatively
(Scollon & King, 2004). It may be that the positive social perception of happy individuals depends on the particular situations in
which individuals encounter them. An apparently happy person
may be judged quite favorably in an acquaintanceship situation,
but an incongruously happy person may well be judged more
negatively. The capacity to downregulate positive affect may be a
facet of social skill and effective self-regulation (e.g., M. W. Erber
& Erber, 2001; R. Erber & Erber, 2000).
Although existing evidence indicates that happy individuals
indeed tend to be successful across a number of life domains, we
must be mindful that negative emotions can be functional under
some circumstances. Individuals who are temperamentally prone
to greater levels of negative emotions may help their groups under
some conditions. In addition, happiness in some circumstances
may be sacrificed in order to reach long-term goals. We do not yet
fully understand the limits of the success of happy people, and we
do not know the extent to which the effects of positive affect are
dependent on culture and cultural norms for emotion. The current
findings indicate that happy people are in many ways successful
people. This does not mean, of course, that happy people show
superior performance in every activity and situation.
Final Remarks
We have reviewed extensive evidence demonstrating that happy
people are successful and flourishing people. Part of the explanation for this phenomenon undoubtedly comes from the fact that
success leads to happiness. Our review, however, focuses on the
846
reverse causal direction—that happiness, in turn, leads to success.
Happy people show more frequent positive affect and specific
adaptive characteristics. Positive affect has been shown, in experimental, longitudinal, and correlational studies, to lead to these
specific adaptive characteristics. Thus, the evidence seems to support our conceptual model that happiness causes many of the
successful outcomes with which it correlates. Furthermore, the
data suggest that the success of happy people may be mediated by
the effects of positive affect and the characteristics that it promotes. It appears that happiness, rooted in personality and in past
successes, leads to approach behaviors that often lead to further
success. At the same time, happy people are able to react with
negative emotions when it is appropriate to do so.
The desire to be happy is prevalent in Western culture (e.g.,
Diener, Suh, Smith, & Shao, 1995; King & Broyles, 1997), and a
happy life is very much the preferred life (King & Napa, 1998). If
subjective well-being feels good but otherwise leaves people impaired, for example, in terms of decision making, social relationships, physical health, or success in life, we might question its net
value for society and for the individual. In this article, we reviewed
cross-sectional, longitudinal, and experimental data showing that
happy individuals are more likely than their less happy peers to
have fulfilling marriages and relationships, high incomes, superior
work performance, community involvement, robust health, and a
long life. The three classes of evidence also indicated that positive
emotions, as well as chronic happiness, are often associated with
resources and characteristics that parallel success and thriving—
that is, desirable behaviors and cognitions such as sociability,
optimism, energy, originality, and altruism. Although our conclusions run counter to the belief that successful outcomes and desirable characteristics are primarily the causes, rather than the consequences, of happiness, a surprisingly large amount of evidence
now appears to challenge this belief.
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Received March 5, 2004
Revision received March 11, 2005
Accepted April 12, 2005 䡲
New Editor Appointed, 2007–2012
The Publications and Communications (P&C) Board of the American Psychological Association
announces the appointment of a new editor for a 6-year term beginning in 2007. As of January 1,
2006, manuscripts should be directed as follows:
• Emotion (www.apa.org/journals/emo.html), Elizabeth A. Phelps, PhD, Department of Psychology, New York University, 6 Washington Place, Room 863, New York, NY 10003.
Electronic manuscript submission. As of January 1, 2006, manuscripts should be submitted
electronically via the journal’s Manuscript Submission Portal (see the Web site listed above).
Authors who are unable to do so should correspond with the editor’s office about alternatives.
Manuscript submission patterns make the precise date of completion of the 2006 volumes uncertain.
The current editors, Richard J. Davidson, PhD, and Klaus R. Scherer, PhD, will receive and consider
manuscripts through December 31, 2005. Should 2006 volumes be completed before that date,
manuscripts will be redirected to the new editor for consideration in 2007 volume.
Review of General Psychology
2005, Vol. 9, No. 2, 111–131
Copyright 2005 by the Educational Publishing Foundation
1089-2680/05/$12.00 DOI: 10.1037/1089-2680.9.2.111
Pursuing Happiness: The Architecture of Sustainable Change
Sonja Lyubomirsky
Kennon M. Sheldon
University of Missouri—Columbia
David Schkade
University of California, San Diego
The pursuit of happiness is an important goal for many people. However, surprisingly
little scientific research has focused on the question of how happiness can be increased
and then sustained, probably because of pessimism engendered by the concepts of
genetic determinism and hedonic adaptation. Nevertheless, emerging sources of optimism exist regarding the possibility of permanent increases in happiness. Drawing on
the past well-being literature, the authors propose that a person’s chronic happiness
level is governed by 3 major factors: a genetically determined set point for happiness,
happiness-relevant circumstantial factors, and happiness-relevant activities and practices. The authors then consider adaptation and dynamic processes to show why the
activity category offers the best opportunities for sustainably increasing happiness.
Finally, existing research is discussed in support of the model, including 2 preliminary
happiness-increasing interventions.
The pursuit of happiness holds an honored
position in American society, beginning with
the Declaration of Independence, where it is
promised as a cherished right for all citizens.
Today, the enduring U.S. obsession with how to
be happy can be observed in the row upon row
of popular psychology and self-help books in
any major bookstore and in the millions of
copies of these books that are sold. Indeed,
many social contexts in the United States have
the production of happiness and positive feelings as their primary purpose, and questions
Sonja Lyubomirsky, Department of Psychology, University of California, Riverside; Kennon M. Sheldon, Department of Psychology, University of Missouri—Columbia;
David Schkade, Rady School of Management, University of
California, San Diego.
This work was supported in part by grants from the
Positive Psychology Network. We are grateful to Linda
Houser-Marko, Kathleen Jamir, and Chris Tkach for conducting library research and to Shelley Taylor, David Sherman, and the other members of Psychology 421 for valuable
comments on a draft.
Correspondence concerning this article should be addressed to Sonja Lyubomirsky, Department of Psychology,
University of California, Riverside, CA 92521, or Kennon
M. Sheldon, Department of Psychological Sciences, 112
McAlester Hall, University of Missouri, Columbia, MO
65211. E-mail: [email protected] or sheldonk@missouri
.edu
such as “Are you happy?” and “Are you having
fun?” fit nearly every occasion (Markus &
Kitayama, 1994). Not surprisingly, the majority
of U.S. residents rate personal happiness as very
important (Diener, Suh, Smith, & Shao, 1995;
Triandis, Bontempo, Leung, & Hui, 1990) and
report thinking about happiness at least once
every day (Freedman, 1978). Furthermore, the
pursuit of happiness is no longer just a North
American obsession, but instead it is becoming
ever more global as people seek to fulfill the
promises of capitalism and political freedom
(Diener et al., 1995; Freedman, 1978; Triandis
et al., 1990). It seems that nearly all people
believe, or would like to believe, that they can
move in an “upward spiral” (Sheldon & HouserMarko, 2001) toward ever greater personal
well-being.
Is the pursuit of happiness merely a bourgeois
concern, a symptom of Western comfort and
self-centeredness, a factor that has no real impact on psychological adjustment and adaptation? The empirical evidence suggests that this
is not the case. Indeed, a number of researchers
and thinkers have argued that the ability to be
happy and contented with life is a central criterion of adaptation and positive mental health
(e.g., Diener, 1984; Jahoda, 1958; Taylor &
Brown, 1988). Bolstering this notion, Lyubomirsky and her colleagues recently com-
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LYUBOMIRSKY, SHELDON, AND SCHKADE
piled evidence showing that happiness has numerous positive byproducts that appear to benefit individuals, families, and communities
(Lyubomirsky, King, & Diener, 2004; see also
Fredrickson, 2001). Furthermore, Lyubomirsky
et al.’s analysis revealed that happy people gain
tangible benefits in many different life domains
from their positive state of mind, including
larger social rewards (higher odds of marriage
and lower odds of divorce, more friends, stronger social support, and richer social interactions; e.g., Harker & Keltner, 2001; Marks &
Fleming, 1999; Okun, Stock, Haring, & Witter,
1984), superior work outcomes (greater creativity, increased productivity, higher quality of
work, and higher income; e.g., Estrada, Isen, &
Young, 1994; Staw, Sutton, & Pelled, 1995),
and more activity, energy, and flow (e.g., Csikszentmihalyi & Wong, 1991).
Further supporting the argument that subjective happiness may be integral to mental and
physical health, happy people are more likely to
evidence greater self-control and self-regulatory
and coping abilities (e.g., Aspinwall, 1998;
Fredrickson & Joiner, 2002; Keltner & Bonanno, 1997), to have a bolstered immune system
(e.g., Dillon, Minchoff, & Baker, 1985; Stone et
al., 1994), and even to live a longer life (e.g.,
Danner, Snowdon, & Friesen, 2001; Ostir,
Markides, Black, & Goodwin, 2000). Also,
happy people are not just self-centered or selfish; the literature suggests that happy individuals instead tend to be relatively more cooperative, prosocial, charitable, and “other-centered”
(e.g., Isen, 1970; Kasser & Ryan, 1996; Williams & Shiaw, 1999).
In summary, happy individuals appear more
likely to be flourishing people, both inwardly
and outwardly. Thus, we argue that enhancing
people’s happiness levels may indeed be a worthy scientific goal, especially after their basic
physical and security needs are met. Unfortunately, however, relatively little scientific support exists for the idea that people’s happiness
levels can change for the better. For example,
the happiness-boosting techniques proposed in
the self-help literature generally have limited
grounding in scientific theory and even less
empirical confirmation of their effectiveness
(Norcross et al., 2000). Consider a representative best seller, You Can Be Happy No Matter
What: Five Principles for Keeping Life in Perspective, by Carlson (1997). Do the five princi-
ples work? Do some work better than others?
Do the principles work better for some people
than for others? Are any positive effects of the
principles due, ultimately, to placebo effects? If
the book actually helps people “get happier,”
does the happiness boost last? Although it is
possible that some of the advice given in this
and other similar books could well be appropriate and effective, the authors provide almost no
empirical research in support of their claims.
One receives little more guidance from contemporary academic psychology. Of course, research psychologists have identified many predictors of people’s happiness or subjective wellbeing. For example, well-being has been shown
to be associated with a wide variety of factors,
including demographic status (e.g., Argyle,
1999; Diener, Suh, Lucas, & Smith, 1999; Myers, 2000), personality traits and attitudes (e.g.,
Diener & Lucas, 1999), and goal characteristics
(e.g., McGregor & Little, 1998). However, a
limitation of previous research is that the vast
majority of studies have been cross sectional
and have reported between-subjects effects
rather than investigating well-being longitudinally and examining within-subject effects. In
addition, very few happiness intervention studies have been conducted. Thus, researchers still
know surprisingly little about how to change
well-being, that is, about the possibility of “becoming happier.” Doubtless, part of the reason
for this neglect is the difficulty of conducting
longitudinal and intervention studies. The problem is further compounded by the tendency of
applied mental health researchers to focus on
pathology rather than on positive mental health
(Seligman & Csikszentmihalyi, 2000) and by
the thorny issues raised when theorists speculate
on how people “should” live their lives to maximize their potential for happiness (Schwartz,
2000). However, we believe the principal reason for the neglect of this question is the considerable scientific pessimism over whether it is
even possible to effect sustainable increases in
happiness.
Historical Sources of Pessimism
Three considerations serve to illustrate the
depth of this pessimism. First is the idea of a
genetically determined set point (or set range)
for happiness. Lykken and Tellegen (1996)
have provided evidence, based on twin studies
SPECIAL ISSUE: ARCHITECTURE OF SUSTAINABLE HAPPINESS
and adoption studies, that the heritability of
well-being may be as high as 80% (although a
more widely accepted figure is 50%; Braungart,
Plomin, DeFries, & Fulker, 1992; Tellegen et
al., 1988; cf. Diener et al., 1999). Whatever the
exact coefficient, its large magnitude suggests
that for each person there is indeed a chronic or
characteristic level of happiness. Consistent
with this idea, Headey and Wearing (1989)
found, in a four-wave panel study, that participants tended to keep returning to their own
baselines over time (see also Suh, Diener, &
Fujita, 1996). Thus, although there may be substantial variation around this baseline level in
the short term, in the long term people perhaps
cannot help but return to their set point, or to the
middle of their set range: “What goes up must
come down” (a more detailed description of the
happiness set point is provided later).
A second and closely related source of pessimism comes from the literature on personality
traits. Traits are cognitive, affective, and behavioral complexes that are, by definition, consistent across situations and across the life span
and therefore may account for part of the stability of the set point. In support of the latter
assumption, McCrae and Costa (1990) have
shown impressive long-term stability for the
“Big Five” traits, including the two traits most
closely related to well-being: neuroticism and
extraversion. Specifically, people tend to maintain the same rank ordering in their levels of
worry, rumination, and guilt, as well as in their
levels of social engagement, enthusiasm, and
self-confidence. Because of the close relation
between psychological well-being and these
personality characteristics, McCrae and Costa
argued that people also tend to maintain the
same relative level of happiness over time (see
also Costa, McCrae, & Zonderman, 1987; Diener & Lucas, 1999).
A third source of pessimism arises from the
concept of the hedonic treadmill (Brickman &
Campbell, 1971), which suggests that any gains
in happiness are only temporary, because humans so quickly adapt to change (see also Kahneman, 1999; Tversky & Griffin, 1991). Thus,
although new circumstances may temporarily
cause people to become happier or sadder, they
rapidly adjust, and the effect of these new circumstances on happiness then diminishes
quickly or even disappears entirely. For example, Brickman, Coates, and Janoff-Bulman
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(1978) showed that, after 1 year, lottery winners
were no happier than controls, and furthermore
recent paralysis victims were not as unhappy as
one would expect. Further evidence of hedonic
adaptation comes from findings of remarkably
small correlations between happiness and
wealth (Diener & Lucas, 1999) and Myers’s
(2000) observation that while U.S. citizens’ personal income has more than doubled in the
past 50 years, their happiness levels have remained the same. The notion of an individual
fighting against the effects of adaptation brings
to mind an image of a pedestrian walking up a
descending escalator. Although the improving
circumstances of her life may propel her upward
toward ever greater happiness, the process of
adaptation forces her back to her initial state.
Together, these concepts and findings suggest
that trying to become happier may be as futile as
trying to become taller (Lykken & Tellegen,
1996). Indeed, some have argued that pursuing
happiness may backfire altogether, if the pursuit
becomes a conscious “extrinsic” goal that distracts people from enjoying the moment
(Schooler, Ariely, & Loewenstein, in press; see
also Sheldon, 2004). Moreover, striving for
happiness may inevitably result in deep disappointment for many people. From this perspective, rather than seeking an upward spiral,
maybe people would be better off simply accepting their current personality and happiness
levels (McCrae & Costa, 1994). In Zen terms,
perhaps one should try to transcend the pursuit
of happiness rather than trying to maximize it
(Gaskins, 1999). Indeed, a number of philosophical traditions embrace the notion that happiness should not be increased beyond an ideal
level, one akin to a “Golden Mean” (Aristotle,
1974) between agony and ecstasy. To be sure,
most people would undoubtedly reject an unrestrained, ceaseless pursuit of well-being.
Present Sources of Optimism
Is the pursuit of happiness futile? We believe
not. Despite the seemingly compelling reasons
we have listed for pessimism regarding attempts
to elevate levels of well-being, there are also
compelling reasons for optimism. In the following, we briefly describe four sources of optimism, returning to consider some of them in
greater detail later. First, some researchers have
had success, albeit limited and short term, in
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using interventions to increase happiness (e.g.,
Fava, 1999; Fordyce, 1977, 1983, Lichter,
Haye, & Kammann, 1980; Sheldon, Kasser,
Smith, & Share, 2002). The potential of happiness-enhancing interventions is further reflected
in emerging research in the positive psychology
tradition demonstrating that practicing certain
virtues, such as gratitude (Emmons & McCullough, 2003), forgiveness (McCullough, Pargament, & Thoresen, 2000), and thoughtful selfreflection (King, 2001; Lyubomirsky, Sousa, &
Dickerhoof, 2004), can bring about enhanced
well-being. Furthermore, research documenting
the long-term effectiveness of cognitive and
behavioral strategies to combat negative affect
and depression has encouraging implications
for the possibility of elevating long-term happiness (e.g., Gloaguen, Cottraux, Cucherat, &
Blackburn, 1998; Jacobson et al., 1996).
Second, many different motivational and attitudinal factors have been linked to well-being,
factors that are presumably amenable to some
volitional control. Examples of possible motivational factors include the successful pursuit of
life goals that are intrinsic in content (e.g.,
Kasser & Ryan, 1996); concordant with a person’s interests, motives, and values (Brunstein,
Schultheiss, & Grassman, 1998; Sheldon & Elliot, 1999; Sheldon & Kasser, 1995); and internally consistent (e.g., Emmons & King, 1988;
Sheldon & Kasser, 1995). Examples of potentially controllable attitudinal factors include the
tendency to take an optimistic perspective on
one’s life situations (e.g., DeNeve & Cooper,
1998; McCrae & Costa, 1986), the inclination
to avoid social comparisons and contingent selfevaluations (e.g., Lyubomirsky & Ross, 1997),
and the tendency to feel a sense of optimism or
efficacy regarding one’s life (Bandura, 1997;
Scheier & Carver, 1993; Seligman, 1991; Taylor & Brown, 1988).
A third reason for optimism is provided by
recent findings that older people tend to be
somewhat happier than younger people
(Charles, Reynolds, & Gatz, 2001; Diener &
Suh, 1998; Roberts & Chapman, 2000; Sheldon
& Kasser, 2001). Specifically, both cross-sectional and longitudinal work has shown that
older persons report higher life satisfaction and
lower negative affect. Although these main effects do not always emerge, they are observed
frequently enough to suggest that greater happiness can indeed be achieved over time, not
just by a few people but perhaps by the majority
of people. Indeed, Carstensen’s (1995) socioemotional selectivity theory suggests that older
people learn to structure their lives and pursue
particular goals that maximize positive emotions, consistent with the proposal that people
can learn to sustainably increase their wellbeing. Further supporting this notion are Sheldon and Kasser’s (2001) results, which showed
that age-related increases in well-being are in
part mediated by volitional changes, including
older people’s ability to select more enjoyable
and self-appropriate goals.
Yet another reason why genes are not necessarily destiny is that they appear to influence
happiness indirectly, that is, by influencing the
kinds of experiences and environments one has
or seeks to have. Thus, unwanted effects of
genes could be minimized by active efforts to
steer oneself away from situations that detract
from well-being or by avoiding being enticed
toward maladaptive behaviors (Lykken, 2000;
Lyubomirsky, 2001). In addition, it is worth
noting that heritability coefficients describe covariations, not mean levels. Furthermore, even a
high heritability coefficient for a particular trait
(such as happiness) does not rule out the possibility that the mean level of that trait for a
specific population can be raised. Under the
right conditions, perhaps anyone can become
happier, even if her or his rank ordering relative
to others remains stable.
To summarize, it appears there is a paradox:
Some theoretical perspectives and empirical
data suggest that happiness can be increased,
whereas other theories and data imply that it
cannot. How can these conflicting perspectives
on the possibility of happiness enhancement be
resolved? Also, if enhanced happiness is indeed
possible, what kinds of circumstances, activities, or habits of mind are most likely to bring
gains, especially gains that can be maintained?
Model of Happiness
Accordingly, the primary question addressed
in this article is the following: Through what
mechanisms, if any, can a chronic happiness
level higher than the set point be achieved and
sustained? To this end, we describe the architecture of sustainable happiness. The integrative model of happiness we present accommodates the role of both personality/genetic and
circumstantial/demographic factors in happiness. However, it also goes beyond these crosssectional or concurrent factors to incorporate
dynamic, time-sensitive factors. This extension
allows the question of within-subject change in
well-being, and maintained change, to be addressed. Most important, the model incorporates the role of motivational and attitudinal
factors, consistent with the assumption that happiness can be actively pursued. We attempt to
show that certain types of intentional activities
indeed offer ways to achieve sustainable
changes in well-being, despite the counteracting
effects of adaptation.
In the sections to follow, we first provide a
working definition of chronic happiness. Then
we define the three factors that affect it (genetic
set point, circumstances, and activities) and argue that intentional activities offer the best potential route to higher and sustainable levels of
happiness. Subsequently, we consider some
more complex issues pertaining to the achievement of sustainable well-being via intentional
activity, such as the role of person–activity fit,
optimal timing and variety of activity, and the
supportive role of sustained effort and positive habits. Then, in the final section of the
article, we describe several preliminary efforts
to increase happiness, based on our model,
and discuss the nature of effective happiness
interventions.
Defining Happiness
Here we define happiness as it is most often
defined in the literature, that is, in terms of
frequent positive affect, high life satisfaction,
and infrequent negative affect. These three constructs are the three primary components of
subjective well-being, according to Diener and
colleagues (for reviews, see Diener, 1984, 1994;
Diener et al., 1999). Supporting the legitimacy
of considering them as indicators of the same
underlying construct, we find that the measures
are highly correlated and typically yield a single
factor after negative affect has been recoded
(Sheldon & Kasser, 1998, 2001; Sheldon &
Lyubomirsky, 2004). To refer to this group of
measures, we use the term happiness or subjective well-being, although we also discuss mood
and life satisfaction at times according to the
specific ideas and data being presented.
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It is important to note as well that we use a
subjectivist definition of happiness, one that
commonly relies on people’s self-reports. We
believe this is appropriate and even necessary
given our view that happiness must be defined
from the perspective of the person. In other
words, happiness is primarily a subjective phenomenon for which the final judge should be
“whoever lives inside a person’s skin” (Myers
& Diener, 1995, p. 11; see also Diener, 1994).
However, the fact that the judgment of happiness is necessarily subjective does not mean that
influences on that judgment cannot be studied
empirically; for example, researchers might investigate the effects of factors such as a person’s recent experiences of positive emotion
(Frijda, 1999), the frame in which the question
is presented (Larsen & Fredrickson, 1999), the
meaning that the person ascribes to the question
(Schwarz & Strack, 1999), and the person’s
sense of making satisfactory progress toward
life goals at the time of the judgment (Carver &
Scheier, 1990). We consider some of these factors in greater detail in a later section. Finally,
the fact that self-reported happiness is subjective does not mean that it is unrelated to relatively more “objective” variables. For example,
research has shown significant convergence of
self-reported well-being with peer and spouse
reports of well-being (e.g., Lyubomirsky &
Lepper, 1999; Sandvik, Diener, & Seidlitz,
1993), with recall of particular types of events
(e.g., Seidlitz, Wyer, & Diener, 1997), with
smiling behavior (e.g., Harker & Keltner,
2001), and with physiological responses (e.g.,
Lerner, Taylor, Gonzalez, & Stayn, 2002).
Chronic Happiness Level
Our primary focus in this article is on a
person’s characteristic level of happiness during
a particular period in his or her life, which we
term the chronic happiness level. We define
happiness this way because we wish to identify
a quantity that is more enduring than momentary or daily happiness but that is also somewhat
malleable over time and, thus, amenable to
meaningful pursuit. According to this definition, although it is possible to alter one’s
chronic happiness level, it is much more difficult to do so than to alter one’s happiness level
at a particular moment or on a particular day.
Operationally, one might define a person’s
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chronic happiness level in terms of his or her
retrospective summary judgments regarding his
or her mood and satisfaction during some recent
period (such as the past 2, 6, or 12 months) or as
the average of momentary judgments of mood
and satisfaction made at several times during
the selected period. It is worth adding, however,
that people may vary in their “hedonic profiles,”
such that two individuals with similar chronic
happiness levels might differ in their relative
levels of contentment with life versus their relative frequency of experiencing positive and
negative mood states.
Determinants of the Chronic Happiness
Level
We focus on three primary types of factors
that we believe causally affect the chronic happiness level, namely, the set point, life circumstances, and intentional activity. We focus on
these three factors because they have historically received the majority of attention in the
well-being literature, providing a substantial research base. We also focus on this three-factor
distinction because it allows us to address several important issues and paradoxes, such as the
question of whether it is even possible to “become happier” given strong genetic influences
on happiness, the question of why past wellbeing research has revealed such weak associations between demographic/circumstantial
variables and happiness, and the question of
how a person might appropriately take action to
“pursue” happiness.
Figure 1 provides an illustration of the ap-
Figure 1. Three primary factors influencing the chronic
happiness level.
proximate percentage of the variance that each
of the three factors accounts for in cross-sectional well-being, as suggested by past research.
As can be seen in the pie chart, existing evidence suggests that genetics account for approximately 50% of the population variation (Braungart et al., 1992; Lykken & Tellegen, 1996;
Tellegen et al., 1988), and circumstances account for approximately 10% (Argyle, 1999;
Diener et al., 1999). This leaves as much as
40% of the variance for intentional activity,
supporting our proposal that volitional efforts
offer a promising possible route to longitudinal
increases in happiness. In other words, changing
one’s intentional activities may provide a happiness-boosting potential that is at least as large
as, and probably much larger than, changing
one’s circumstances. In the following, we provide a definition of each factor, briefly consider
whether and how changing that factor can lead
to changes in people’s chronic well-being, and
discuss whether such changes may be sustainable over the long term, that is, whether the
forces of hedonic adaptation can be counteracted by that factor.
Happiness Set Point
We assume that an individual’s chronic happiness level is in part determined by her or his
set point, which is defined as the central or
expected value within the person’s set range.
The happiness set point is genetically determined and is assumed to be fixed, stable over
time, and immune to influence or control. Consistent with this assumption, twin studies
(Lykken & Tellegen, 1996; Tellegen et al.,
1988), long-term panel studies (Headey &
Wearing, 1989), and studies of the effects of life
events on well-being (Brickman et al., 1978) all
indicate substantial long-term stability in happiness. For example, Lykken and Tellegen
(1996) assessed well-being in twins at 20 years
of age and then again at 30 years of age. The
test–retest correlation was a considerable .50.
Even more important, the cross-twin, cross-time
correlation for the happiness of monozygotic
twins was .40 (or 80% of the test–retest correlation), suggesting that the heritability of the
“stable” component of happiness is approximately .80. In contrast, the cross-twin, crosstime correlation for dizygotic twins was close to
zero (.07). Other studies, although differing in
their estimates of heritability, have consistently
shown that monozygotic twins exhibit considerably more similar patterns of happiness
change than do dizygotic twins, providing converging support that the variance in adult happiness is in large part determined genetically.
The set point probably reflects relatively immutable intrapersonal, temperamental, and affective personality traits, such as extraversion,
arousability, and negative affectivity, that are
rooted in neurobiology (e.g., Ashby, Isen, &
Turken, 1999; Davidson, 1999; Depue & Collins, 1999; Gray, 1990; Kagan, 2003; Robinson,
Emde, & Corley, 2001), are highly heritable
(Tellegen et al., 1988), and change little over
the life span (McCrae & Costa, 1990). For example, Kagan has followed children from 4
months to 11 years of age and shown that sociability in 11-year olds can be traced to a
particular type of infant temperament (called
“low reactive”) that appears to involve a distinct
neurochemical profile. Other writers, including
Gray and Depue, have also compiled persuasive
evidence for the neurobiological underpinnings
of personality. This rapidly growing body of
research supports the set point theory of personality and affect.
Implications of the Set Point for
Sustainable Increases in Chronic
Happiness
The preceding analysis implies that one’s
chronic happiness during a particular life period
can be increased, but not by changing one’s set
point, because by definition it is constant. In
other words, although it is possible that future
scientists will learn how to alter people’s basic
temperaments and dispositions, at present it appears that focusing on the set point is not a
fruitful avenue for happiness increase. Again,
however, one can posit that nongenetic factors
also influence a person’s chronic happiness
level, helping to determine whether the person
falls in the lower or upper portion of his or her
potential range at a particular time. The remaining variables in the model are designed to represent these other factors.
Circumstances
This category consists of happiness-relevant
circumstantial factors, that is, the incidental but
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relatively stable facts of an individual’s life.
Happiness-relevant circumstances may include
the national, geographical, and cultural region
in which a person resides, as well as demographic factors such as age, gender, and ethnicity (see Diener et al., 1999, for a review). Circumstantial factors also include the individual’s
personal history, that is, life events that can
affect his or her happiness, such as having experienced a childhood trauma, being involved in
an automobile accident, or winning a prestigious award. Finally, circumstantial factors include life status variables such as marital status,
occupational status, job security, income,
health, and religious affiliation.
Again, previous cross-sectional research has
linked all of the circumstantial factors just described to subjective well-being (Diener et al.,
1999). For example, empirical evidence shows
that people who are paid more are relatively
happier (e.g., Diener, Sandvik, Seidlitz, & Diener, 1993) and that middle-class individuals are
somewhat happier than working-class individuals (e.g., Warr & Payne, 1982). Married people
are happier than those who are single, divorced,
or widowed (e.g., Mastekaasa, 1994), even in
cultures as diverse as those of Belarus and Spain
(Diener, Gohm, Suh, & Oishi, 2000). Findings
also reveal that religiously committed people
are relatively more likely to rate themselves as
“very happy” (Gallup, 1984) and that, not surprisingly, healthy people, especially older ones,
declare themselves to be slightly happier than
sick people (e.g., Okun et al., 1984).
However, as suggested earlier, all circumstances combined account for only 8% to 15%
of the variance in happiness levels (Argyle,
1999; Diener et al., 1999). These relatively
weak associations have been deemed surprising
and paradoxical, given well-being researchers’
initial expectations that circumstantial factors
such as income and physical health would be
strongly related to happiness (Diener et al.,
1999). We believe that these counterintuitively
small effects can be largely accounted for by
hedonic adaptation and the fact that people
adapt rapidly to new circumstances and life
events. This appears to be the case because
adaptation—whether it is sensory (e.g., to a foul
odor or a heavy weight; Brown, 1953), physiological (e.g., to very hot or cold temperatures;
Dar, Ariely, & Frank, 1995), or hedonic (e.g., to
a salary raise; Brickman et al., 1978; Parducci,
118
1995)— occurs in response to stimuli that are
constant or repeated. By definition, constancy is
a feature of most circumstantial changes.
Implications of Circumstances for
Happiness
Of the different types of circumstances, life
status variables in particular seem to offer some
potential for increasing chronic happiness, in
that individuals often have considerable control
over them. For example, a college football
player may sign a lucrative NFL contract, a
middle-aged divorcee may remarry, or a retired
couple may move to Florida to a condominium
with a view, all becoming happier as a result.
Will such new happiness last, however? Perhaps not, because, as mentioned earlier, hedonic
adaptation tends to shuttle people back to their
starting point following any positive circumstantial change. For example, Headey and
Wearing (1989) found in their four-wave panel
study that positive and negative events (e.g.,
“made lots of new friends,” “got married,” “experienced serious problems with children,” or
“became unemployed”) influenced life satisfaction, positive affect, and negative affect as
would be expected but that people kept returning to their original baselines. And Schkade and
Kahneman (1998) revealed that although “living in California” is a seductive notion for
many, it does not actually make people any
happier in the long run. Furthermore, Lucas,
Clark, Georgellis, and Diener (2003) showed
that, for most people, the life satisfaction benefits derived from getting married tended to fade
over the years. Thus, although one may gain a
temporary “boost” by moving to a new region,
increasing one’s income level, or changing
one’s appearance, such boosts will probably not
last, because people tend to adapt to constant
circumstances. Other reasons why circumstantial changes may prove ineffectual for permanently increasing happiness include the fact that
circumstantial changes can be costly (e.g., in
terms of money, resources, and time) and, in
many cases, impractical or even impossible.
Also, once a realistic “ceiling” of positive circumstances is reached, it may be difficult to
improve matters further.
In short, the data suggest that changes in
circumstances have limited potential for pro-
ducing sustainable changes in chronic happiness. Although this strategy may work in the
short term, it probably will not work in the long
term. Of course, if people have not achieved
basic subsistence and security, then it is logical
for them to attend to these circumstances and
basic needs first, before focusing on maximizing their happiness. However, we assume that,
at best, satisfying basic needs can move people
only up to their set point, not beyond.
Intentional Activity
Now we turn to the third and arguably most
promising means of altering one’s happiness
level: intentional activity. This is a very broad
category that includes the wide variety of things
that people do and think in their daily lives.
Obviously, humans are very active creatures,
with innumerable behaviors, projects, and concerns to which they devote energy. By “intentional,” we mean discrete actions or practices in
which people can choose to engage (although
the choice to initiate the activity may have become habitual, as discussed in a later section).
We also assume that intentional activities require some degree of effort to enact. That is, the
person has to try to do the activity; it does not
happen by itself. Indeed, this point touches on
one of the critical distinctions between the category of activity and the category of life circumstances; that is, circumstances happen to
people, and activities are ways that people act
on their circumstances.
There is good reason to believe that intentional activity can influence well-being. For example, some types of behavioral activity, such
as exercising regularly or trying to be kind to
others, are associated with well-being (e.g.,
Keltner & Bonanno, 1997; Magen & Aharoni,
1991), as are some types of cognitive activity,
such as reframing situations in a more positive
light or pausing to count one’s blessings (Emmons & McCullough, 2003; King, 2001; Seligman, 1991), and some kinds of volitional activity, such as striving for important personal goals
(Sheldon & Houser-Marko, 2001) or devoting
effort to meaningful causes (M. Snyder &
Omoto, 2001). Notably, it is impossible to fully
separate behavioral, cognitive, and volitional
activity; still, we believe the distinction is useful, and we continue to use it throughout the
article.
Implications of Intentional Activity for
Happiness
Again, it appears that increasing one’s set
point and changing one’s life circumstances are
not fruitful avenues for sustainable increases in
chronic happiness. What, if anything, can provide such an avenue? In the following, we argue
that intentional behavioral, cognitive, or volitional activity offers the best potential route.
Some work has already investigated the impact
of adopting new behaviors on longitudinal wellbeing, showing, for example, that faithfully engaging in a new exercise program positively
boosts people’s mood and vitality and can even
maintain the boosts for as long as 6 months
(e.g., Ransford & Palisi, 1996; Stewart et al.,
1997). Although little work has directly investigated the longitudinal effects of changing
one’s cognitive attitudes and practices on enhanced well-being, the general success of cognitive– behavioral therapy in reducing suffering
(Gloaguen et al., 1998) and recent work indicating positive effects of prompting people to
practice positive psychological “virtues” such
as gratitude (Emmons & McCullough, 2003),
hope (C. R. Snyder, Ilardi, Michael, & Cheavens, 2000), and forgiveness (McCullough et al.,
2000) suggest that cognitive activity offers
many excellent possibilities for happiness interventions (Fordyce, 1983).
Turning to the third type of intentional activity, recent longitudinal studies have focused
specifically on volitional activity as a producer
of enhanced well-being (see Sheldon, 2002, for
a review). In such studies, students are typically
asked to pursue self-generated personal goals
over the course of a semester. High levels of
goal progress or attainment consistently predict
increased well-being (i.e., higher positive affect
and life satisfaction and lower negative mood)
from the beginning to the end of the semester,
whereas low levels of progress predict reduced
well-being (Brunstein, 1993; Sheldon, 2002).
Specifically, Sheldon’s longitudinal research in
this area (Sheldon & Elliot, 1998, 1999; Sheldon & Kasser, 1995, 1998) has shown that
well-being increases are most likely when a
person chooses and attains self-concordant
goals, that is, goals that “fit” the person (as
described subsequently). This work has also
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highlighted one potential mediator from successful volitional activity to enhanced well-being, namely, accumulations of positive daily
experiences along the way. The question of
what other proximal factors may mediate
changes in chronic happiness is addressed in
more detail in a later section.
Notably, these studies do not extend beyond a
single span of time. Thus, they do not directly
address the crucial question raised by the current article: whether gains in well-being last.
Although Headey and Wearing’s important
(1989) work suggests that gains in happiness do
not last, notably, their study focused only on life
events (“circumstances,” in our model) and did
not take intentional activity into direct account.
Recently, Sheldon and Houser-Marko (2001)
addressed the question of sustainability by examining the effects of goal attainment on emotional well-being over two consecutive semesters. Consistent with earlier studies, they found
that students who attained their personal goals
during the first semester of their freshman year
experienced enhanced adjustment and emotional well-being at the end of that semester.
More important, they found that students could
maintain their enhanced level of well-being, but
only if they continued to do well at their goals
during the second semester. In contrast, students who did well in the first semester but not
in the second semester tended to regress back to
their original well-being levels. This study offers direct support for our assumption that happiness can be enhanced and then maintained at
the new level, especially when volitional activity is effectively pursued over long periods of
time. Further supporting this conclusion, Sheldon and Lyubomirsky (2004) recently resurveyed these participants 3 years after the original study and found that initially high-performing students had maintained their earlier gains
in emotional well-being throughout their college career.
But what about adaptation? Is it not the case
that even the most successful striver adapts to
his or her happy situation eventually? More
generally, is it not the case that people ultimately adapt to the positive effects of any activity in which they engage, whether it be behavioral, cognitive, or volitional, so that the
activity loses its potency over time?
Although hedonic adaptation undoubtedly
constrains the happiness-inducing effects of in-
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tentional activities, just as it does for circumstances, this adaptation effect appears to be
weaker in the case of activity, as shown by
recent data. For example, Sheldon and Lyubomirsky (2004) recently conducted several
short-term longitudinal studies in which participants’ well-being (positive affect, negative affect, and life satisfaction) was measured at
Time 1, and positive circumstantial and activity-based life changes were measured at Time 2.
Well-being was then measured twice more, at
Times 3 and 4. These investigators found consistent support for a path model, displayed in
Figure 2, in which both positive circumstantial
change and positive activity change predicted
enhanced life satisfaction and positive affect at
Time 3, but only positive activity change predicted maintained happiness gains at Time 4,
with positive circumstantial change dropping
out of the model. In other words, consistent with
the present model, only activity-based well-being change lasted; circumstance-based happiness change did not.
In a separate study, Sheldon and Lyubomirsky (2004) randomly assigned participants to
report on either activity-based positive changes
or circumstantially based positive changes in
their lives. Relative to those in the circumstantial-change group, those in the activity-change
group reported a weaker sense of “having gotten
used to the change, such that it does not give the
same boost as before,” and more strongly endorsed the statement “the change is something
that varies over time, that is, something that
adds variety to my life.” These findings further
support the claim that activity changes are characterized by less hedonic adaptation than circumstantial changes. Parenthetically, Sheldon
and Lyubomirsky’s (2004) findings, taken as a
whole, support the validity of our distinction
Figure 2. Longitudinal path model predicting maintained
changes in well-being from positive circumstantial changes
and positive activity changes. Asterisks indicate p ⬍ .01.
between circumstantial changes and activity
changes. Although the boundaries between
these categories can be fuzzy, apparently they
are clear enough to produce the predicted
effects.
Specific Advantages of Intentional Activity
What are the sources of the sustainable happiness gains afforded by intentional activity?
We posit that activity-based change, unlike circumstance-based change, has several desirable
features that may help to combat adaptation.
Intentional activity is episodic. One feature
of activities is that they are, by definition, episodic and transient; after all, people cannot
spend all of their time doing one thing. This in
itself suggests that individuals may adapt less
readily to new activities than to new circumstances. The episodic nature of activity also
suggests that an additional way to maximize the
impact of an activity is to attend to the timing of
that activity. For example, a person might
choose to “count her blessings” only after braving a difficult period, or only when she is especially needful of a boost. Suppose instead that
she counts the same blessings every day, in a
nonvarying routine. This person may become
bored with the routine and cease to extract
meaning from it. The length of time before one
reengages in a happiness-boosting activity is an
important part of its potency in the next application. By being mindful of the “refractory period” (Kalat, 2001) after which a recently performed activity regains its full happiness-inducing potential, individuals may maximize the
benefits of the activity over time and avoid
reducing or eliminating the activity’s effectiveness through overuse. Thus, people should
strive to discover the optimal timing for each
activity, that is, a frequency of engagement that
allows that activity to remain fresh, meaningful,
and positive for a particular person.
Intentional activity can be varied. Another
important parameter of behavioral, cognitive,
and volitional activities is that people can continually vary them, both in their foci and in the
ways they engage in them. This may help to
reduce adaptation to the activity, allowing it to
retain its potency (McAlister, 1982). Indeed, by
definition, adaptation does not occur to stimuli
that are variable or changeable but only to those
that are constant or repeated (cf. Frederick &
Loewenstein, 1999). For example, a scientist
may regularly ask new questions and become
involved in new projects. In the process, she
often feels the joy of making fascinating new
discoveries and thus may remain particularly
happy (i.e., at the upper end of her potential
range) over a long period of time. If the person
counting her blessings varies the domains of life
in which she counts them (i.e., in relationships,
in work, in her health, or in her most recently
successful domain), then the strategy may remain “fresh” and meaningful and work indefinitely. Supporting this notion, past research
suggests that people tend to seek variety in their
behavior (McAlister, 1982; Ratner, Kahn, &
Kahneman, 1999), perhaps because change—in
both thoughts and actions—is innately pleasurable and stimulating (Berlyne, 1970; Rolls et
al., 1981).
Intentional activity can directly counteract
adaptation. Yet another advantage of intentional activity is that it can directly tackle the
problem presented by adaptation. For example,
the cognitive practice of pausing to savor the
good things in one’s life can directly counteract
the effects of hedonic adaptation to one’s constant circumstances by drawing attention to the
features that produced the initial happiness
boost and helping to keep them from being
taken for granted. As another example, practiced meditators frequently report renewed appreciation of the ordinary as a result of their
intentional reencounters with the world.
The fact that intentional activity can directly
counteract adaptation and the hedonic treadmill
helps shed further light on the distinction between life circumstances and intentional activities. Obviously, many personal characteristics
are both. For example, “being married” and
“being a student” both denote demographic status, yet they also reflect particular sorts of activities. From our perspective, the crucial distinction with respect to well-being is whether
one exerts intentional effort with respect to the
circumstantial category, that is, whether one
acts upon the circumstance (e.g., using intentional practices to keep the circumstance
“fresh”). For example, an individual can engage
in a number of intentional activities with respect
to the circumstantial category “marriage”: A
husband can have the goal of making his marriage work (a volitional activity), he can make
the effort to appreciate his wife’s positive qual-
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ities (an attitudinal activity), and he can try to
remember to bring her flowers (a behavioral
activity). A person who performs these activities would probably best counteract adaptation
to this particular circumstance and derive the
most benefit from it. In contrast, consider a
husband who is not intentionally engaged in his
marriage; for him, this demographic circumstance would essentially become a background
factor, to which adaptation is very likely.
For all of these reasons, intentional activity
appears to offer the best prospects for increasing
and sustaining happiness. Of course, following
through on new intentions, such as the ubiquitous “New Year’s resolutions,” is not necessarily easy (Sheldon & Elliot, 1998). Indeed, we
assume that happiness-increasing strategies can
be initiated and effectively pursued only with
concerted, consistent commitment and effort.
Still, activity-based factors are, by definition,
under greater potential control by the individual
than are genetic, demographic, and most life
status factors. In other words, if anything can do
it, intentional activity can.
Implementing Happiness-Increasing
Strategies
In this section, we briefly consider several
important issues pertaining to how intentional
activity might be used for increasing happiness. In other words, having established that
activity can potentially sustainably elevate
happiness, how might one put this potential to
work? We discuss these strategic issues in
roughly chronological order, proceeding from
the question of how to choose a particular
happiness-boosting activity to the question of
how such activity may be initiated and the
question of how the activity can be maintained over time to produce a sustained increase in the chronic level of happiness. In the
process, we discuss the issue of person–strategy fit, the meaning and nature of effort, the
definition and role of habits, and the impact of
short-term versus long-term considerations.
Choosing an Activity: The Role of
Person–Activity Fit
Any one particular activity will not help every person become happier. People have endur-
122
ing strengths, interests, values, and inclinations
that undoubtedly predispose them to benefit
more from some strategies than others. For example, extraverts may benefit most from activities that bring them into regular contact with
other people, and people high in nurturance
motivation may benefit most from activities that
afford them opportunities to take care of others.
This general “matching” hypothesis (Harackiewicz & Sansone, 1991) is supported by much
recent work showing that the positive effects of
goal attainment on well-being are moderated by
goal–person fit (Brunstein et al., 1998; Diener &
Fujita, 1995; Sheldon & Elliot, 1999; Sheldon
& Kasser, 1998). It is also supported by past
well-being intervention research. For example,
in several studies that instructed participants to
apply 14 different techniques to increase their
personal happiness, the particular techniques
considered most effective for raising happiness
varied greatly from one individual to another
and appeared to be determined by each participant’s needs and areas of specific weakness
(Fordyce, 1977, 1983).
The fit of an activity with a person might be
conceptualized in a variety of ways, for example, with respect to individuals’ motive dispositions, basic needs, core values, signature
strengths, personal resources, hedonic profiles,
or other individual-difference characteristics.
There are also a variety of ways that fit might be
operationalized, such as in terms of self-reported fit, in terms of consistency between implicit and explicit measures of activity-relevant
motives, or in terms of informant-rated person–
activity fit. Another approach is to assume that
certain kinds of experiences are likely to be
beneficial to anyone, because these experiences
reflect universal psychological needs. From this
point of view, any activity that provides certain
experiences, such as those involving belongingness (Baumeister & Leary, 1995), self-efficacy
(Bandura, 1997), or autonomy (Deci & Ryan,
2000), might be assumed to “fit” the person, a
priori.
Role of Effort
Initiating an activity. We assume that engaging in an activity requires at least two different kinds of effort: first, the effort required to
initiate the activity and, second, the effort required to actually carry out and maintain the
activity. This distinction is necessary because it
is clear that many activities have definite positive effects if the person can only get started
doing them. For example, exercising in the
morning, making time to work on at least one
important project during the day, or pausing to
count one’s blessings at the end of the day can
have significant benefits, but only if the person
can “get over the hurdle” of remembering to do
them and overcoming any obstacles to initiating
them. Obviously, those who do not implement
their activity intentions stand a worse chance of
benefiting from them than those who do! We
assume that this kind of self-regulatory effort
requires considerable self-discipline and willpower. Furthermore, such effort may constitute
a limited resource, one that must be marshaled
carefully; in Muraven and Baumeister’s (2000)
terms, self-regulatory will is like a “muscle”
that has limited capacity in a given unit of time
and must be used strategically to avoid fatigue.
If this analogy is accurate, then it seems
logical that some people develop the muscle to
a greater extent than others, thus attaining a
greater ability to “get started” on their intentions
and gaining greater happiness potential. Of
course, some activities will appear intrinsically
more appealing and will be easier to jumpstart;
this is undoubtedly one advantage of selecting
an activity that fits one’s personality. For example, rather than running on a track, a fitnessseeking wilderness lover might instead choose
to run on a trail through the woods, thereby
feeling much less initial resistance to beginning
the activity. As another example, rather than
learning classical pieces, a jazz-loving piano
student might instead choose to work on jazz
standards, enhancing the intrinsic appeal of sitting down to practice.
Maintaining an activity. This brings us to
the second type of effort. Obviously, if a particular activity is to yield sustained happiness
change, the person must keep performing the
activity over the long term. For many effective
happiness-enhancing activities, this will not be
difficult, because the task will probably be inherently interesting or rewarding and thus will
be “autotelic” in nature (Deci & Ryan, 2000),
that is, self-reinforcing and self-sustaining. This
is especially true to the extent that the person
continually varies what he or she does. If, for
example, a person shifts attention among several projects at work, explores new trails in the
state park, or seeks out interesting new piano
pieces, his or her activities should remain intrinsically enjoyable and conducive to many rewarding “flow” experiences (Csikszentmihalyi, 1990).
What if the activity is not enjoyable and thus
difficult to maintain? In this case, stopping the
activity may not be problematic, because it
probably is not working anyway. By emphasizing the importance of enjoying one’s intentional
activity, however, we do not mean to imply that
people should seek out only “fun” activities.
Sometimes choosing to endure boring or even
aversive experiences in the short term can have
considerable positive effects on chronic happiness in the long term; for example, studying for
an important exam in a tedious but required
class may well represent an excellent investment in one’s future chronic happiness, even
though it may detract from one’s momentary
happiness. As another example, a naval officer
candidate is paying a short-term cost (boot
camp) to receive a longer term benefit (a career
as an officer).
Of note, self-determination theory (Deci &
Ryan, 2000; Sheldon, Joiner, & Williams,
2003) posits that the crucial factor in such cases
is whether the person has internalized the nonenjoyable activity, that is, whether he or she is
able to find meaning and value expression in it,
even if it is not pleasant to perform. From this
perspective, the naval officer candidate would
pay a smaller short-term cost if he could undergo boot camp thinking that “this is important
and valuable” rather than thinking that “this is
unnecessary and stupid.” The question of when
and how to sacrifice short-term happiness in
exchange for longer term happiness is an important one, as is the question of how to promote internalization of important happiness-relevant activities that are not intrinsically enjoyable. These questions represent promising
directions for future research.
Role of Habitual Activity
If activities such as “looking on the bright
side,” “making time for the things that matter,”
and “working on an important life goal” make a
difference for happiness, then it seems it would
be a good idea to make a habit of doing them.
However, on the surface, habits appear to
present a conundrum for our model. Is it not the
case that acquiring a habit means that one has
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turned a formerly conscious activity into an
unconscious routine, practiced automatically
and without variation? If so, is it not the case
that one is especially likely to experience hedonic adaptation to that activity, such that it loses
its happiness-boosting potential?
We think not. However, to illustrate, we must
first distinguish between the habit of regularly
initiating a potentially beneficial activity and
the habit of implementing it the same way every
time (the two types of effort mentioned earlier).
We assume that hedonic adaptation occurs only
with respect to particular experiences, and not
with respect to the decisions that give rise to
those experiences. Thus, making a habit out of
deciding to initiate an activity is not problematic but may instead help people to keep getting
“over the hump.” For example, a woman might
make running an automatic part of her daily
routine, such that she does not even have to
make the decision of whether or not to run each
day, thus deriving considerable benefit. What is
potentially problematic is when people make a
habit out of how they implement the activity.
When this happens, the flow of experiences
produced by such a habit is likely to remain
relatively constant, and thus, adaptation is likely
to have the most pernicious effects. To overcome this, as suggested earlier, people should
mindfully attend to optimal timing and variety
in the ways they practice an activity. For example, the woman might want to vary the route,
time of day, and speed of her running. This will
help forestall the effects of adaptation.
Extensions and Further Questions
Now that we have presented our basic conceptual model of sustainable changes in happiness, we briefly consider a variety of additional
issues that extend beyond this basic model.
What are the key ingredients of particular activities that lead a person to a higher level of
well-being? Although this question is somewhat
peripheral to our model, it merits brief discussion. We assume that happiness increases come
from at least two sources that are described,
respectively, by bottom-up and top-down theories of well-being (Diener, 1994). Bottom-up
theories postulate that people make global wellbeing judgments in part with reference to emotions associated with their recent experiences
(Kahneman, 1999). If they can recall a large
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number of recent affectively positive experiences, then they report being very happy (see
Sheldon & Elliot, 1999, for supporting data).
Studies have produced support for this bottom-up perspective by showing that accumulations of need-satisfying daily experiences over
time (such as competence, relatedness, and autonomy; Deci & Ryan, 2000) lead to enhanced
global well-being at the end of that time (Reis,
Sheldon, Ryan, Gable, & Roscoe, 2000; Sheldon, Ryan, & Reis, 1996). Furthermore, Sheldon and Lyubomirsky (2004) found, in their
comparison of the sustained effects of circumstantial changes and activity changes on
changes in well-being among students, that the
more enduring activity-based effects on happiness were mediated by the greater feelings of
competence and relatedness associated with activity changes during the semester.
But what about when people say they are
happy despite having had recent negative emotional experiences? Although bottom-up theories cannot account for this, top-down theories
can. According to such models, well-being
judgments are in part determined by global attitudinal or meaning-based factors. Thus, a person who “suffers for a cause” might still feel
very happy because her suffering demonstrates
her commitment to, and also perhaps moves her
closer to obtaining, an important life goal. As
another example, a man who has a bad day at
work might still report being very happy that
night, because of a short but meaningful visit
from his grandchildren that evening that helped
him to reframe the day. Again, we believe that
intentional activity can lead to new well-being
by both top-down and bottom-up routes, that is,
both via accumulations of small positive experiences and via a sense of global meaning and
purpose.
Preliminary Data: Happiness Interventions
The model of sustainable happiness that we
have proposed has clear implications for how to
design interventions for increasing happiness.
Before describing these, we first discuss some
of the few happiness intervention studies that
have been conducted, to show their general consistency with our approach. As noted earlier,
Fordyce (1977, 1983) conducted several happiness intervention studies in which he taught 14
happiness-relevant strategies to students as part
of their coursework. All of the strategies fit into
one or more of the three categories of activity
outlined earlier: behavioral (e.g., “spend more
time socializing”), cognitive (e.g., “become
present oriented”), and volitional (e.g., “get better organized and plan things out”). Consistent
with our conceptual model, Fordyce found that
the strategies worked; that is, a significant main
effect of participation was found for the experimental conditions. Again, intentional activity
can successfully increase happiness. Also consistent with our model, he found that some
strategies worked better than others and, in addition, that person–strategy fit had a moderating
effect on strategy effectiveness.
More recently, Sheldon and colleagues
(2002) conducted an intervention study based
on participants’ personal goals. Early in the
semester, they taught experimental participants
a set of four strategies for enhancing their experience and attainment of their personal goals:
“own the goal,” “make it fun,” “keep a balance,” and “remember the big picture.” Consistent with the studies cited earlier, goal attainment predicted increases in well-being at the
end of the semester. Interestingly, there was no
main effect of experimental condition on increased well-being or goal attainment. Instead, a
significant interaction was observed, such that
only participants whose goals “fit” their interests and values benefited from the intervention.
In other words, those with self-concordant goals
who received the intervention evidenced the
greatest goal attainment and, thus, the greatest
increase in well-being. In addition to demonstrating that happiness-boosting interventions
can work for at least some people, this finding
provides further support for our proposition that
the fit of the activity to the person makes a
difference.
Obviously, much future work remains to be
done regarding happiness-increasing interventions, particularly research that explicitly manipulates the various factors in our model. As a
preliminary test, Lyubomirsky, Tkach, and
Sheldon (2004) recently conducted two 6-week
happiness-enhancing interventions based in behavioral and cognitive–attitudinal change.
Drawing on promising interventions grounded
in the positive psychology tradition—that is,
focused on building positive affect and personal
strengths rather than on reducing or coping with
negative affect, pathology, or weakness—they
used two diverse strategies (one cognitive and
one social behavioral) to serve as initial existence proofs of our conceptual model. To this
end, experimental participants were prompted
to perform kind acts or to pause and “count their
blessings.”
The strategy of committing acts of kindness
was expected, on the basis of previous theory
and research, to boost temporary moods and
long-lasting well-being. For example, individuals who report a greater interest in helping others, an inclination to act in a prosocial manner,
or intentions to perform altruistic or courteous
behaviors are more likely to rate themselves as
dispositionally happy (see Lyubomirsky, King,
& Diener, 2004, for a review). We assume that
acts of kindness and generosity can boost happiness in a variety of ways. Such acts may foster
a charitable perception of others and one’s community, an increased sense of cooperation and
interdependence, and an awareness of one’s
good fortune. In addition, people who commit
acts of kindness may begin to view themselves
as altruistic people, as well as to feel more
confident, efficacious, in control, and optimistic
about their ability to help. Furthermore, acts of
generosity can inspire greater liking by others,
along with appreciation, gratitude, and prosocial reciprocity (Trivers, 1971), all of which are
valuable in times of stress and need. Finally,
kind behaviors may help satisfy a basic human
need for relatedness (Baumeister & Leary,
1995), thereby contributing to increased
happiness.
Thus, in their first intervention, Lyubomirsky, Tkach, and Sheldon (2004) asked students
to perform five acts of kindness per week over
the course of 6 weeks, either all five acts in 1
day or five acts spread over the week. Such acts
were described as behaviors that benefit other
people or make others happy, usually at some
cost to oneself (e.g., donating blood, helping a
friend with a paper, visiting an elderly relative,
or writing a thank-you note to a former professor). A no-treatment control group simply completed measures of well-being immediately before the intervention and immediately after. The
results, displayed in the top panel of Figure 3,
provided preliminary evidence that a short-term
happiness-enhancing activity can increase wellbeing. Furthermore, supporting our model’s
predictions, Lyubomirsky et al. found that optimal timing was critical. Whereas control par-
125
Figure 3. Changes in well-being over the course of two
6-week interventions: performing acts of kindness (top) and
counting one’s blessings (bottom).
ticipants experienced a reduction in happiness
over the course of the 6-week period, participants who committed acts of kindness experienced a significant increase in well-being, but
this increase was evident only among those who
showed their weekly generosity all in a single
day. Because many of the kind acts that students
performed were small ones, spreading them
over the course of a week might have diminished their salience and power or made them
less distinguishable from participants’ habitual
kind behavior.
The second intervention tested a cognitive
happiness-increasing activity. Recently, Emmons and McCullough (2003) found that practicing grateful thinking on a regular basis can
enhance concurrent well-being. Gratitude promotes the savoring of positive life experiences
and situations so that maximum satisfaction and
enjoyment are distilled from one’s circumstances. As noted earlier, this practice may directly counteract the effects of hedonic adaptation by helping people extract as much appreciation from the good things in their lives as
possible. In addition, the ability to appreciate
their life circumstances may also be an adaptive
126
coping strategy by which people positively reinterpret stressful or negative life experiences,
bolster coping resources, and strengthen social
relationships. Finally, the practice of gratitude
appears to be incompatible with negative emotions and thus may reduce feelings of envy,
anger, or greed.
Thus, in the second 6-week intervention, students were instructed to contemplate “the things
for which they are grateful” either once a week
or three times a week. Examples of “blessings”
listed by students included “a healthy body,”
“my mom,” and “AOL instant messenger.”
Control participants completed only the happiness assessments. The results again suggested
that short-term increases in happiness are possible and, furthermore, that optimal timing is
important. In summary, students who regularly
expressed gratitude showed increases in wellbeing over the course of the study relative to
controls, but these increases were observed only
among students who performed the activity just
once a week (see Figure 3, bottom panel). Perhaps counting their blessings several times a
week led people to become bored with the practice, finding it less fresh and meaningful over
time.
Although the results of these two interventions are encouraging, they notably did not test
the sustainability of the well-being increases for
the experimental groups (i.e., “kindness” and
“blessings”) and did not examine the impact of
key moderators of activity effects. In the future,
in addition to assessing the efficacy of different
activities for producing sustainable increases in
well-being, we will investigate the effects of
such potential moderators as fit, effectiveness,
timing, variety, cultural membership, social
support, and the habits associated with the
activity.
What are the most general recommendations
for increasing happiness suggested by our
model? Simply, happiness seekers might be advised to find new activities to become engaged
in, preferably activities that fit their values and
interests. They should make a habit out of initiating the activity while at the same time varying their focus and timing in terms of the way
they implement the activity. People might be
advised to avoid basing their happiness on the
acquisition of particular circumstances or objects (e.g., buying a luxury car or moving to
California), because they will tend to habituate
to such stable factors. Again, however, one can
deter, or at least delay, such adaptation to positive circumstantial changes by engaging in intentional effort and activity with respect to
them. That is, if one can remember to appreciate
or actively engage with the object or circumstance (i.e., pause to savor the new Mercedes or
take advantage of the California weather), then
stable objects and circumstances may not be
stable after all, from a phenomenological perspective. Thus, it remains the case that only life
changes involving intentional activity can be
expected to lead to sustainable changes in
well-being.
Conclusion
If it is meaningful and important to pursue
happiness, then it is crucial to find out how this
can be accomplished. To what extent, and how,
can people succeed in making themselves happier? In this article, we have attempted to integrate what is known about happiness change,
especially longitudinal variations in well-being,
into a single summary model. The model encompasses a wide variety of findings and suggests a number of new directions for research.
More than two centuries have passed since the
“pursuit of happiness” was proclaimed as a divinely ordained human right. We believe it is
finally time for the issue of sustainable wellbeing to be given the scientific attention that it
deserves.
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Received September 25, 2004
Accepted September 28, 2004 䡲
Journal of Personality and Social Psychology
2002, Vol. 83, No. 5, 1178 –1197
Copyright 2002 by the American Psychological Association, Inc.
0022-3514/02/$5.00 DOI: 10.1037//0022-3514.83.5.1178
Maximizing Versus Satisficing: Happiness Is a Matter of Choice
Barry Schwartz and Andrew Ward
John Monterosso
Swarthmore College
University of Pennsylvania
Sonja Lyubomirsky
Katherine White and Darrin R. Lehman
University of British Columbia
Can people feel worse off as the options they face increase? The present studies suggest that some
people—maximizers— can. Study 1 reported a Maximization Scale, which measures individual differences in desire to maximize. Seven samples revealed negative correlations between maximization and
happiness, optimism, self-esteem, and life satisfaction, and positive correlations between maximization
and depression, perfectionism, and regret. Study 2 found maximizers less satisfied than nonmaximizers
(satisficers) with consumer decisions, and more likely to engage in social comparison. Study 3 found
maximizers more adversely affected by upward social comparison. Study 4 found maximizers more
sensitive to regret and less satisfied in an ultimatum bargaining game. The interaction between maximizing and choice is discussed in terms of regret, adaptation, and self-blame.
principles of rational choice (e.g., J. Baron, 2000; Kahneman &
Tversky, 1979, 1984; Tversky, 1969; Tversky & Kahneman, 1981;
for a discussion, see Schwartz, 1986, 1994). In particular, modern
behavioral economics has acknowledged that the assumption of
complete information that characterizes rational choice theory is
implausible. Rather than assuming that people possess all the
relevant information for making choices, choice theorists treat
information itself as a “commodity,” something that has a price (in
time or money), and is thus a candidate for consumption along
with more traditional goods (e.g., Payne, 1982; Payne, Bettman, &
Johnson, 1993).
Almost a half century ago, Simon (1955, 1956, 1957) suggested
an approach to explaining choice that was more cognizant of
human cognitive limitations than rational choice theory. Simon
argued that the presumed goal of maximization (or optimization) is
virtually always unrealizable in real life, owing both to the complexity of the human environment and the limitations of human
information processing. He suggested that in choice situations,
people actually have the goal of “satisficing” rather than maximizing. To satisfice, people need only to be able to place goods on
some scale in terms of the degree of satisfaction they will afford,
and to have a threshold of acceptability. A satisficer simply encounters and evaluates goods until one is encountered that exceeds
the acceptability threshold. That good is chosen. In subsequent,
accidental encounters with other goods in the relevant domain, the
scale of acceptability enables one to reject a formerly chosen good
for a higher ranked one should that one turn up. A satisficer thus
often moves in the direction of maximization without ever having
it as a deliberate goal. Simon’s alternative to rational choice theory
questions not only the processes by which options are assessed and
choices made, but also the motives that underlie choice. To satisfice is to pursue not the best option, but a good enough option.
Rational choice theory has tried to explain preference and
choice by assuming that people are rational choosers (von Neumann & Morgenstern, 1944). According to the rational choice
framework, human beings have well-ordered preferences—preferences that are essentially impervious to variations in the way the
alternatives they face are described or the way in which they are
packaged or bundled. The idea is that people go through life with
all their options arrayed before them, as if on a buffet table. They
have complete information about the costs and benefits associated
with each option. They compare the options with one another on a
single scale of preference, or value, or utility. And after making the
comparisons, people choose so as to maximize their preferences,
or values, or utilities.
Although the science of economics has historically depended on
the tenets of rational choice theory, it is now well established that
many of the psychological assumptions underlying rational choice
theory are unrealistic and that human beings routinely violate the
Barry Schwartz and Andrew Ward, Department of Psychology, Swarthmore College; John Monterosso, Department of Psychology, University of
Pennsylvania; Sonja Lyubomirsky, Department of Psychology, University
of California, Riverside; Katherine White and Darrin R. Lehman, Department of Psychology, University of British Columbia, Vancouver, British
Columbia, Canada.
This research was facilitated by support from the Positive Psychology
Network (M. Seligman, Director), an intramural grant from Swarthmore
College to Barry Schwartz, a sabbatical grant from the Solomon Asch
Center for Study of Ethnopolitical Conflict to Andrew Ward, an intramural
grant from the University of California to Sonja Lyubomirsky, a Social
Sciences and Humanities Research Council (SSHRC) doctoral fellowship
to Katherine White, and grants from SSHRC and the Natural Sciences and
Engineering Research Council to Darrin R. Lehman.
Correspondence concerning this article should be addressed to Barry
Schwartz, Department of Psychology, Swarthmore College, 500 College Avenue, Swarthmore, Pennsylvania 19081. E-mail: bschwar1@
swarthmore.edu
Can There Be Too Much Choice?
There is no question that greater choice can provide benefits for
the chooser. Indeed it is axiomatic in rational choice theory that
1178
MAXIMIZING VS. SATISFICING AND WELL-BEING
people cannot have too many options. If, for example, one is trying
to decide between two models of a CD player, and then discovers
that a third model also is available, the third model may be just the
thing one is after. If not, one can simply go back to deliberating
between the first two. And one can always ignore the new, third
option altogether. So it seems irrational to perceive oneself as
worse off as a result of added possibilities for choice. Nonetheless,
there is now a small body of evidence suggesting that added
options are a mixed blessing (e.g., Simenson & Tversky, 1992;
Tversky & Shafir, 1992). Results have begun to appear in the
decision-making literature indicating that adding options can make
a choice situation less rather than more attractive for people—that
indeed, sometimes people prefer it if others make the choices for
them (Beattie, Baron, Hershey, & Spranca, 1994).
In one series of studies (Iyengar & Lepper, 2000; see also
Iyengar & Lepper, 1999), participants were more likely to purchase exotic jams or gourmet chocolates when they had 6 options
from which to choose than when they had 24 or 30, respectively.
And perhaps more importantly, those with fewer options expressed
greater satisfaction with the choices they made. Similarly, university students were more likely to write an extracredit essay, and
wrote better essays, when they had 6 topics to choose from than
when they had 30. Iyengar and Lepper suggested several possible
factors that may underlie this effect. One is the avoidance of
potential regret. The more options there are, the more likely one
will make a nonoptimal choice, and this prospect may undermine
whatever pleasure one gets from one’s actual choice. There is
ample evidence that regret aversion is a potent force in decision
making—perhaps even more potent than the loss aversion that has
been a significant feature of Kahneman and Tversky’s (1979)
prospect theory of decision making (Beattie et al., 1994; Bell,
1982, 1985; Larrick & Boles, 1995; Loomes & Sugden, 1982;
Ritov, 1996; Simenson, 1992; Zeelenberg, 1999; Zeelenberg &
Beattie, 1997; Zeelenberg, Beattie, van der Pligt, & de Vries, 1996;
Zeelenberg et al., 1998).
A second factor that may make increased choice unattractive is
that it creates a seemingly intractable information problem. It is
hard enough to gather information and go through the deliberations
needed to make the best choice among 6 options. To choose the
best among 30 options is truly daunting. So rather than even try,
people may disengage, choosing almost arbitrarily to complete the
process. As a result of this disengagement, many of the psychological processes that normally are recruited to enhance the attractiveness of the choices one makes may not be operative (see
Gilovich & Medvec, 1995, for an account of some of these
processes, in the context of their theory of regret).
Maximizing, Satisficing, and Choice
Schwartz (2000) recently argued that the proliferation of options
can have a variety of negative effects on well-being. He suggested
that as options are added within a domain of choice, three problems materialize. First, there is the problem of gaining adequate
information about the options to make a choice. Second, there is
the problem that as options expand, people’s standards for what is
an acceptable outcome rise. And third, there is the problem that as
options expand, people may come to believe that any unacceptable
result is their fault, because with so many options, they should be
able to find a satisfactory one. Similar problems arise as choice
1179
becomes available in domains in which previously there was no
choice. No matter how dissatisfied one is with one’s telephone
service, if phone service is provided by a regulated monopoly, one
cannot do better, and inadequate service is not one’s fault. However, when choice of phone service becomes available, there is no
longer any reason to tolerate inadequate service, and failure to
obtain adequate service is one’s responsibility. Schwartz (2000)
suggested that people might in general be better off with constrained and limited choice than with unconstrained choice.
However, expanded opportunities for choice need not have these
negative psychological effects. Consider the different effects that
an expanding array of options might have on two people, one of
whom aims to maximize his or her outcomes in that domain and
one of whom aims to satisfice. For the maximizer, added options
pose problems. One cannot be sure that one is making the maximizing choice without examining all the alternatives. And if it is
impossible or impractical to examine all the alternatives, then
when the maximizer gives up the search and chooses, there will be
a lingering doubt that he or she could have done better by searching a bit more. Thus, as options proliferate, the likelihood of
achieving the goal of maximization goes down. Further, the potential for regret is ever present, because the question the maximizer is asking him- or herself is not “is this a good outcome?” but
“is this the best outcome?”.
Expanded opportunities for choice may have different effects on
the satisficer. The satisficer is looking for something that crosses
the threshold of acceptability—something that is good enough.
Adding options in a domain in which the satisficer has already
encountered something good enough need have no effect; the new
options may simply be ignored. With “good enough” rather than
the “best” as a criterion, the satisficer will be less inclined to
experience regret if it turns out that an option better than the
chosen one was available. And if no satisfactory option has been
encountered in a domain, added options will provide new possibilities for finding something that crosses the “good enough”
threshold. Thus, the risk of being made worse off by added options
may be minimal for satisficers.
Are some people maximizers and others satisficers? Do people
differ in the nature of the goals they pursue in choice situations?
And if so, do people also differ in their sensitivity to potential
regret? Is it concern about potential regret that influences some
people to be maximizers? We addressed these questions in the
present series of studies by creating survey instruments designed to
distinguish maximizers from satisficers and to measure sensitivity
to regret. And if people do differ in these respects, does it make a
difference? We addressed this question in the present studies in
several ways. Study 1 examined the relations between one’s scores
on a Maximization Scale and a Regret Scale and scores on measures of happiness, life satisfaction, optimism, depression, neuroticism, and perfectionism. In subsequent studies we attempted to
validate some of these putative relations and to identify possible
mediators. Study 2, guided by the notion that maximizers might
seek more information than satisficers when making decisions,
used a questionnaire to examine relations between maximization
and the amount of social comparison that goes into making purchasing decisions, as well as the satisfaction people derive from
those decisions. Study 3, inspired by findings reported by Lyubomirsky and Ross (1997) that unhappy people are more affected
by upward social comparison than happy people, further explored
SCHWARTZ ET AL.
1180
the relation between maximizing and social comparison processes
by examining whether maximizers and satisficers respond differentially to social comparison manipulations. Finally, Study 4 examined the possible causal role of regret in mediating between
maximizing and dissatisfaction by exposing participants to a computer game designed to manipulate the potential for regret. We
anticipated that maximizers would be more sensitive to regret than
satisficers, and would derive less satisfaction from their results in
games in which the opportunity for regret was salient.
Study 1. Maximizing, Satisficing, and Regret:
Scale Development
This study involved the creation and evaluation of two new
measuring instruments— one designed to assess the tendency to
satisfice or maximize and one designed to assess the tendency to
experience regret. Materials were administered to seven samples of
participants, four of them university students and three of them
community adults. In total, 1,747 participants completed the maximizing and regret questionnaires.
Method
Overview
Packets of questionnaires were administered to seven samples (total
N ⫽ 1,747). Participants completed the questionnaires either in small
groups of 3 to 7 (Samples 1 and 3), during one large, group session
(Samples 2, 4, 5, and 7), or individually (Sample 6). Completing the
questionnaires required less than 45 min (in the case of Sample 6, less
than 15 min). For each administration, the content of the questionnaires
varied, as described below.
Participants
Each of the first four samples comprised students in introductory psychology courses, who received course credit for their participation. The
first two samples (n ⫽ 82 and n ⫽ 72) were recruited at Swarthmore
College, the third sample (n ⫽ 100) at the University of California,
Riverside, and the fourth sample (n ⫽ 401) at the University of British
Columbia, Vancouver, Canada. The fifth sample (n ⫽ 752) consisted of
participants at a 1-day seminar for allied health care professionals (mean
age ⫽ 47 years), the majority of whom were nurses. The sixth sample (n ⫽
220) was composed of individuals recruited at a large train station in an
urban setting (mean age ⫽ 41 years), and the seventh (n ⫽ 120) comprised
individuals in an urban courthouse approached while waiting to be informed if they would serve on a jury (mean age ⫽ 40 years). There were
approximately equal numbers of males and females in the first three
samples, along with the sixth sample (i.e., individuals at the train station),
but the fourth sample (i.e., students at the University of British Columbia)
included 258 females and 141 males (2 participants failed to identify their
gender), the fifth sample (i.e., health care providers) included 684 females
and 60 males (8 participants failed to identify their gender), and the final
sample (members of a jury pool) included 87 females, 28 males, and 5
individuals who failed to identify their gender. The third, fourth, and
seventh samples also were quite diverse ethnically. The third sample was
39% Asian, 24% Caucasian, 10% Chicano(a)/Latino(a), 8% African American, and 10% other, and the fourth sample (classified using slightly
different categories) was 51% of East Asian descent (e.g., Chinese, Taiwanese), 25% of Western European descent (e.g., British, French), 9% of
East Indian descent (e.g., Indian, Pakistani), and 15% other. The seventh
sample was 48% Caucasian, 45% African American, and 7% other.
Materials
Sample 1. Our aim with the first sample was to create maximization
and regret scales and to investigate correlations between responses to those
scales and well-established measures of well-being. Participants completed
a preliminary 42-item questionnaire designed to measure maximization (33
items) and regret (9 items). Participants responded to each item using a
7-point, Likert-type scale (1 ⫽ completely disagree, 7 ⫽ completely
agree). In addition, they completed a four-item Subjective Happiness Scale
(SHS) designed to assess dispositional happiness (Lyubomirsky & Lepper,
1999), a depression survey (the 13-item short form of the Beck Depression
Inventory [BDI-SF; Beck & Beck, 1972]), and a measure of dispositional
optimism (the Life Orientation Test [LOT; Scheier & Carver, 1985]).
On the basis of item reliability and face validity, the measure was
reduced to 22 items, 17 assessing maximization and 5 assessing regret.
These 22 items were then presented to 11 judges (advanced undergraduate
students majoring either in psychology or economics) who were unaware
of either the purpose of our studies or the specific hypotheses under
investigation. The judges were asked to examine each item and indicate
whether, on the one hand, it probed an individual’s inclination to “get the
best out of any situation” or “settle for good enough,” or, on the other hand,
it probed a person’s sensitivity to “the possibility that he or she might
regret a decision once made.” Of the 5 “regret” items, 4 were judged by 10
of our 11 informants to be about regret and the 5th was judged by 9
informants to be about regret. Of the 17 “maximization” items, 10 were
judged by 10 of 11 informants to be about maximization, 3 were so judged
by 9 informants, and 4 were so judged by 7 informants. Thus, we were
reasonably confident that our measures had face validity—that our understanding of what the questions were asking would be matched by that of the
participants. We then submitted the 5 regret items and 17 maximization
items to a principal-components factor analysis (PCA), which is reported
below.
Sample 2. Participants in the second sample received these 22 items,
unidentified and intermixed. In an independent test of the putative associations investigated in Sample 1, they were also asked to complete the SHS
and the BDI-SF.
Sample 3. Participants in the third sample completed the same 22-item
questionnaire assessing maximization and regret, along with the SHS,
BDI-SF, and LOT. In addition, they completed a questionnaire probing life
satisfaction (the Satisfaction With Life Scale [Diener, Emmons, Larsen, &
Griffin, 1985]) and a scale assessing dispositional Neuroticism (John,
Donahue, & Kentle, 1991), a Big Five trait that we thought might be
correlated with maximization.
Sample 4. The fourth sample of participants completed the 22-item
questionnaire along with the SHS. In addition, these participants were
asked a series of questions regarding tendencies to engage in social
comparison and patterns of purchasing behavior. These materials comprise
the substance of Study 2 and thus discussion of them appears later in the
article.
Sample 5. The fifth sample completed the same 22-item questionnaire
in addition to the aforementioned measures of happiness and depression
(i.e., the SHS and BDI).
Sample 6. The sixth sample also completed the 22-item questionnaire
and the SHS, along with a 15-item perfectionism subscale composed of the
Self-Oriented Perfectionism items of the Multidimensional Perfectionism
Scale (Hewitt & Flett, 1990, 1991). The addition of this scale was intended
to investigate participants’ tendencies to hold exceedingly high standards
for themselves in a variety of domains. The scale included items such as,
“One of my goals is to be perfect in everything I do,” and “I demand
nothing less than perfection of myself” (1 ⫽ disagree, 7 ⫽ agree). In
addition, a subset of participants (n ⫽ 146) completed the 10-item measure
of self-esteem developed by Rosenberg (1965).
Sample 7. The only measures relevant to this article that were completed by members of the prospective jury pool were the 22-item maximi-
zation/regret survey and the same 10-item measure of self-esteem (Rosenberg, 1965).
Results
Factor Analysis
We conducted a PCA on the combined samples (n ⫽ 1,747) to
determine the factor structure of the regret and maximizing items.
We sought the solution that best approximated a simple structure—that is, the one in which most of the items loaded on at least
one factor, and each item loaded on only one factor. What
emerged, on the basis of a varimax rotation, was a six-factor
solution. However, two of the factors contained only two items
each, and one item failed to load on any of the factors. In addition,
the item-total correlations for all but one of these five items were
quite low. We thus eliminated the four items with low item-total
correlations, resulting in a 13-item Maximization Scale and a
five-item Regret Scale. We conducted another PCA on these 18
items. The resulting four-factor solution is presented in Table 1.
The first factor, on which all five regret items loaded, references
“regret,” and makes up the Regret Scale. The other factors are
subcategories of maximizing/satisficing and make up the Maximization Scale. The second and third factors are largely behavioral
examples of maximizing. The second factor includes being open to
better jobs, songs on the radio, television shows, and relationships,
liking lists that rank things, and fantasizing about alternatives to
reality (which also loaded on the “regret” factor, though its loading
was lower than that of the other “regret” items; in addition, this
item was judged by 9 of our 11 informants to be more about
maximizing and satisficing than about regret). The third factor,
which concerns primarily shopping behaviors, includes shopping
for a friend, renting videos, and shopping for clothing. Writing
several drafts of letters so as to word things just right also loads on
this factor. The fourth factor represents having high standards,
both for oneself and for things in general. One of the items that
loaded on this factor also loaded on the “regret” factor. Its loading
on the “regret” factor was substantially lower than all the other
regret items, and also lower than its loading on this “maximizing”
factor. Moreover, this item was judged by 10 of our 11 informants
to be about maximizing. All further analyses, in this and subsequent studies, used responses to the modified, 13-item Maximization Scale rather than the 17-item scale participants actually saw.
The correlation (across all participants) between scores on the 13and the 17-item scales was .99 ( p ⬍ .001). Cronbach’s alpha was
.71 for the Maximization Scale and .67 for the Regret Scale.
Correlations With Standard Personality Measures
Sample 1. Table 2 presents the Pearson’s zero-order correlations between the variables investigated in Study 1. As can be seen
in the table, a tendency for participants to be maximizers rather
than satisficers (␣ ⫽ .70) was significantly correlated with a
tendency to experience more regret and depression, as well as to be
less optimistic, and less happy ( p ⬍ .06). By way of further
illustration, of the 18 people who scored 8 or above on the BDI-SF,
qualifying for a diagnosis of at least mild depressive symptoms
(Beck & Beck, 1972), 8 (44%; Mean BDI score ⫽ 13.25) also
scored in the top quartile for maximization, whereas only 1 (6%;
BDI score ⫽ 8) scored in the bottom quartile. By contrast, of
1181
the 19 people scoring in the top quartile for happiness, 8 (42%;
Mean SHS score ⫽ 16.88) were in the bottom quartile for maximization, whereas only 3 (16%; Mean SHS score ⫽ 16.33) were in
the top quartile.
Sample 2. Table 2 also presents the correlations between the
13-item maximization composite (␣ ⫽ .60) and the five-item
regret composite (␣ ⫽ .78) for Sample 2, along with the BDI-SF
and the SHS—the only other measures administered to this sample. Once again, we observed strong associations between maximization and a tendency to experience regret and depression, and
lower levels of happiness.
Sample 3. Our third sample provided a further opportunity to
investigate relations between maximization and various personality constructs. Table 2 presents the intercorrelations between the
maximization composite (␣ ⫽ .70), the regret composite (␣ ⫽
.70), and measures of optimism, happiness, depression, neuroticism, and satisfaction with life. Correlations between maximizing
and the constructs of regret, depression, and satisfaction with life
were significant beyond the p ⬍ .01 range. In addition, maximizing was negatively correlated with optimism ( p ⬍ .05) and happiness ( p ⬍ .10). However, the relation between maximizing and
neuroticism was not significant ( p ⬎ .10). In sum, in addition to
replicating the results found with Sample 1, this sample provided
evidence for a strong relation between maximization and diminished life satisfaction, as well as a nonsignificant relation with
neuroticism.
Sample 4. Table 2 presents data from our fourth sample on the
relations between the maximization composite (␣ ⫽ .63), the
regret composite (␣ ⫽ .73), and the SHS. Once again, the correlation between maximizing and regret was significant, although the
relation between maximizing and happiness was modest (r ⫽ .10,
p ⬍ .05).
Sample 5. The fifth section of Table 2 displays the correlations
between maximizing (␣ ⫽ .70), regret (␣ ⫽ .74), happiness, and
depression for the sample of health care providers who completed
the relevant measures. As seen with the previous samples, a
tendency to score highly on the Maximization Scale was predictive
of greater regret and depression, as well as lower levels of selfreported happiness.
Sample 6. The sixth section of Table 2 presents correlations
for participants approached at the urban train station. Once again,
maximizing (␣ ⫽ .72) was positively correlated with regret (␣ ⫽
.67), and negatively correlated with happiness. In addition, maximizing was significantly correlated with perfectionism (r ⫽ .25,
p ⬍ .001). And for the subsample who completed the relevant
measure, maximizing and self-esteem were negatively correlated
(r ⫽ ⫺.30, p ⬍ .001). Interestingly, however, whereas maximizing
and perfectionism were significantly correlated, neither happiness
(r ⫽ .12, p ⬍ .08) nor self-esteem (r ⫽ .02, ns) correlated
significantly with perfectionism. Indeed, if anything, the relation
between perfectionism and happiness was positive rather than
negative.
Sample 7. The final sample, taken from prospective jury members, replicated the significant association between maximizing
(␣ ⫽ .73) and self-esteem exhibited by the subsample in Sample 6,
(r ⫽ ⫺.26, p ⬍ .01).
Across the seven samples, maximization scores ranged
from 1.15 to 6.62, with a mean of 3.88 and a median of 3.85. Also,
across all samples, the correlation between maximizing and regret
SCHWARTZ ET AL.
1182
Table 1
Factor Analysis of the Regret and Maximization Scales Using PCA With Varimax Rotation
Factor
F1
F2
F3
F4
Item-total r
Regret Scale
Whenever I make a choice, I’m curious about what would have
happened if I had chosen differently.
.78
.62
Whenever I make a choice, I try to get information about how
the other alternatives turned out.
.74
.57
If I make a choice and it turns out well, I still feel like
something of a failure if I find out that another choice would
have turned out better.
.62
.51
When I think about how I’m doing in life, I often assess
opportunities I have passed up.
.61
.51
Once I make a decision, I don’t look back. (R)
.56
.40
Maximization Scale
When I watch TV, I channel surf, often scanning through the
available options even while attempting to watch one
program.
.81
.45
When I am in the car listening to the radio, I often check other
stations to see if something better is playing, even if I’m
relatively satisfied with what I’m listening to.
.77
.46
I treat relationships like clothing: I expect to try a lot on before
I get the perfect fit.
.51
.33
No matter how satisfied I am with my job, it’s only right for
me to be on the lookout for better opportunities.
.44
.41
.40
.44
.38
.33
I often fantasize about living in ways that are quite different
from my actual life.
.43
I’m a big fan of lists that attempt to rank things (the best
movies, the best singers, the best athletes, the best novels,
etc.).
I often find it difficult to shop for a gift for a friend.
.73
.39
When shopping, I have a hard time finding clothing that I
really love.
.71
.31
Renting videos is really difficult. I’m always struggling to pick
the best one.
.68
.46
I find that writing is very difficult, even if it’s just writing a
letter to a friend, because it’s so hard to word things just
right. I often do several drafts of even simple things.
.57
.33
No matter what I do, I have the highest standards for myself.
.80
.20
I never settle for second best.
.78
.25
.51
.28
Whenever I’m faced with a choice, I try to imagine what all
the other possibilities are, even ones that aren’t present at the
moment.
.36
Note. Item marked by “R” was reverse scored in the analysis. The factor analysis was a principal-components
analysis (PCA) with varimax rotation, using eigenvalues greater than 1 as the extraction method. The last column
displays the corrected item-total correlations for each item with its respective scale (i.e., regret [first five items]
or maximization).
1183
Table 2
Pearson’s Zero-Order Correlations Among Variables in Six Samples
Variable
Max
Regret
SHS
Sample 1 (n ⫽ 82)
Regret
SHS
BDI
LOT
.61***
⫺.21
.24*
⫺.28*
⫺.15
.03
⫺.07
Sample 2 (n ⫽ 72)
Regret
SHS
BDI
.45***
⫺.34**
.44***
⫺.40**
.46***
⫺.55***
Sample 3 (n ⫽ 100)
Regret
SHS
BDI
LOT
NR
SWLS
.36***
⫺.17
.27**
⫺.25*
.16
⫺.27**
⫺.51***
.47***
⫺.35***
.35***
⫺.54***
⫺.66***
.74***
⫺.58***
.71***
Sample 4 (n ⫽ 401)
Regret
SHS
.39***
⫺.10*
⫺.27***
Sample 5 (n ⫽ 752)
Regret
SHS
BDI
.46***
⫺.28***
.31***
⫺.40***
.39***
⫺.66***
Sample 6 (n ⫽ 220)
Regret
SHS
.50***
⫺.17*
⫺.22**
—
—
⫺.46***
.54***
BDI
LOT
NR
—
—
—
—
⫺.51***
—
—
—
—
—
—
⫺.54***
.49***
⫺.68***
⫺.50***
.59***
⫺.48***
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
Note. Dashes indicate that data were not collected for this measure. Max ⫽ Maximization Scale; Regret ⫽
Regret Scale; SHS ⫽ Subjective Happiness Scale; BDI ⫽ Beck Depression Inventory; LOT ⫽ Life Orientation
Test; NR ⫽ Neuroticism; SWLS ⫽ Satisfaction With Life Scale.
* p ⬍ .05. ** p ⬍ .01. *** p ⬍ .001.
was .52 ( p ⬍ .001), and in the samples in which it was assessed,
happiness and maximizing were significantly correlated (r ⫽
⫺.25, p ⬍ .001)—as were maximizing and depression (r ⫽ .34,
p ⬍ .001).
Gender Differences
No gender differences were found in Samples 1–3 or 5 in
participants’ scores on the Maximization Scale, the Regret Scale,
or in the association between maximizing and measures of wellbeing and regret. In Samples 4, 6, and 7, a significant gender
difference emerged—that is, males were more likely than females
to be maximizers in all three of these samples: Sample 4 (Ms
⫽ 4.46 vs. 4.27), t(395) ⫽ 2.41, p ⬍ .02; Sample 6 (Ms ⫽ 4.08
vs. 3.79), t(209) ⫽ 2.26, p ⬍ .05; and Sample 7 (Ms ⫽ 4.33
vs. 3.91), t(107) ⫽ 2.03, p ⬍ .05.
Partial Mediation by Regret
Because questionnaires from Samples 1–3 and 5 included a
common measure of depression and Samples 1– 6 included a
common measure of happiness as well as our maximization and
regret scales, we were able to investigate a putative mechanism
underlying the observed effects, namely, that the relations observed between maximization and both depression and happiness
were mediated by a tendency to experience regret. According to
R. M. Baron and Kenny (1986; see also Martin, Tesser, & McIntosh, 1993), four criteria must be met to establish mediation: (1)
the predictor variable (i.e., maximization) must be related to the
criterion variable (e.g., depression); (2) the mediator (i.e., regret)
must be related to the predictor; (3) the mediator must be related
to the criterion (controlling for the influence of the predictor); and
(4) the relation between the predictor and the criterion must be
eliminated or significantly reduced when the criterion is regressed
simultaneously on the predictor and the mediator. Turning first to
depression, across the four samples, we observed a significant
relation between maximization and scores on the BDI (r ⫽ .34,
p ⬍ .001), meeting the first of the aforementioned criteria. In
addition, regret and maximization were strongly correlated (r ⫽
.52, p ⬍ .001), meeting Criterion 2, and the relation between regret
and depression (r ⫽ .39) remained significant in a regression
SCHWARTZ ET AL.
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equation that controlled for the influence of maximization, F(2,
983) ⫽ 105.45, p ⬍ .001, regret ␤ ⫽ .29; maximizing ␤ ⫽ .19
(meeting Criterion 3). Finally, as this last multiple regression
equation (which regressed depression simultaneously on regret and
maximization) makes clear, although the relation between maximization and depression remained significant after controlling for
regret, consistent with the dictates of Criterion 4, the relation was
significantly weaker than it had been in the absence of regret (i.e.,
a change in beta from .34 to .19), an effect confirmed by a test
based on Sobel’s (1982) method for determining the existence of
a mediational relation (z ⫽ 7.87, p ⬍ .001; see also MacKinnon &
Dwyer, 1993; Preacher & Leonardelli, 2001).
Similar analyses confirmed a mediational role played by regret
in the relation between maximization and happiness, which were
significantly negatively correlated across the six samples (r ⫽
⫺.25, p ⬍ .001). In brief, when regret, which was also negatively
correlated with happiness (r ⫽ ⫺.37), was entered into a regression equation along with maximization as predictors of happiness,
the aforementioned relation between maximization and happiness
was significantly reduced (i.e., a change in beta from ⫺.25 to
⫺.08), as confirmed by a Sobel test of mediation (z ⫽ 10.91, p ⬍
.001). In sum, regret appeared to play a partial mediational role
between maximization and depression and between maximization
and happiness. However, because of high correlations between
regret and other constructs investigated in one or more samples—
constructs such as depression, happiness, and subjective wellbeing—any mediational role ascribed to regret should be viewed
with caution. And, of course, regret was not manipulated in any of
these samples, permitting no causal conclusions to be drawn.
Discussion
Study 1 provided evidence for individual differences in what
people aspire to when they make decisions in various domains of
their lives. Maximizers desire the best possible result; satisficers
desire a result that is good enough to meet some criterion. When
we correlated scores on our Maximization Scale with wellestablished measures of well-being, we found that maximizers
reported significantly less life satisfaction, happiness, optimism,
and self-esteem, and significantly more regret and depression, than
did satisficers. Though Study 1 tells us nothing about the direction
of causality, it is possible that whereas a maximizing decision
strategy might, as a matter of logic, yield better objective outcomes
than a satisficing strategy, it is likely to yield worse subjective
outcomes. Study 1 also revealed that although maximizing was
significantly correlated with perfectionism (Sample 6), the correlations of each of these measures with happiness and self-esteem in
the study were quite different (happiness was negatively correlated
with maximizing and positively correlated with perfectionism;
self-esteem was negatively correlated with maximizing and uncorrelated with perfectionism), suggesting that maximizing and perfectionism are distinct.
Study 1 also tells us nothing about the stability over time of
scores on the Maximization Scale. If a maximizing orientation is
something like a trait, we would expect response patterns to be
stable over time. Although a good deal more research is needed,
Gillham, Ward, and Schwartz (2001) have collected repeated measures from 102 undergraduates, who were given the Maximization
Scale four times over a period of 9 months. Scores at Time 1
correlated with scores at Time 2 (r ⫽ .81), with scores at Time 3
(r ⫽ .82), and with scores at Time 4 (r ⫽ .73). Though larger
samples and longer interevaluation intervals are essential before
any firm conclusions can be drawn, these results suggest that a
maximizing orientation enjoys some degree of stability.
How is a maximizer to judge whether a given outcome is the
best possible outcome? In many cases, there is not a finite and
transparent set of possibilities to allow for complete and unambiguous judgment. For example, what does it mean to have the best
possible salary, meal at a restaurant, wardrobe, or even the best
possible spouse? Although imagination could provide a standard,
a more probable basis for the maximizer’s assessment in these
domains is social comparison (perhaps only with those seen as
belonging to an appropriate comparison group). What does it mean
to have ordered the best possible meal at a restaurant other than
that it is better than anyone else’s meal? Thus, whereas “good
enough” usually can be judged in absolute terms, “the best possible” may often require social comparison. Being a maximizer may
require one to be concerned with one’s relative position.
Festinger (1954) and Frank (1985, 1999; see also Hirsch, 1976)
have argued persuasively that people do seem to be guided largely
by how they are doing relative to relevant others, and several
studies that compared the effects of absolute and relative position
on satisfaction have observed that good relative position produces
greater satisfaction than good absolute position (Bazerman, Loewenstein, & White, 1992; Bazerman, Moore, Tenbrunsel, WadeBenzoni, & Blount, 1999; Blount & Bazerman, 1996; Hsee,
Blount, Loewenstein, & Bazerman, 1999; Solnick & Hemenway,
1998). Poor relative position, however, appears to affect some
people more than others. For example, Lyubomirsky and Ross
(1997) reported that unhappy people are more affected by upward
social comparison than happy people. More specifically, in their
first study, Lyubomirsky and Ross found that whereas both happy
and unhappy people derived satisfaction from information that
their performance was better than that of a peer, only unhappy
people seemed to suffer from information that their performance
was worse than that of a peer.
Especially relevant are the findings from Study 2 of the Lyubomirsky and Ross (1997) article. In that study, happy and
unhappy students received positive or negative feedback from the
experimenter on a novel teaching task, and then witnessed a
same-sex peer receive even more positive or even more negative
feedback than themselves. The most striking finding from this
study was that unhappy students reported feeling happier and more
self-confident when they had received a poor evaluation on their
performance (2 out of 7), but heard their peer receive an even
worse one (1 out of 7), than when they had received an excellent
evaluation (6 out of 7), but heard their peer receive an even better
one (7 out of 7). Happy students, by contrast, did not show this
pattern of “sensitive” responding to comparisons with peers. These
findings lend some credibility to our hypothesis that maximizers
may be more concerned with relative position, and thus with social
comparison, than satisficers, especially in light of the findings
from Study 1 that maximizers are generally less happy than
satisficers.
Studies 2 and 3 were designed to explore directly the relative
importance of social comparison to maximizers and satisficers.
Study 2 inquired about social comparison in the context of purchasing decisions. Study 3 replicated Lyubomirsky and Ross’s
(1997) first study with groups of participants identified as maximizers or satisficers.
Study 2. Maximizing, Satisficing, Social Comparison,
and Consumer Behavior
Because many of the choices that people make in their daily
lives concern the purchase and consumption of goods, Study 2
explored maximizing and satisficing with respect to consumer
purchasing decisions. As indicated in the introduction, a proliferation of options can pose significant problems for a maximizer.
One cannot be sure that one is making the best choice without
examining all the alternatives. And if examination of all the
alternatives is not feasible, then when the maximizer finally
chooses, there may be a lingering doubt that he or she could have
done better with more searching. Thus, as options increase, the
likelihood of successful maximization goes down. Further, the
potential for regret is ever present because the maximizer is asking
“is this the best outcome?” and “could I have done better?” And in
attempting to answer these questions, given the time and
information-processing constraints that everyone faces, maximizers may be inclined to rely on information about how others are
doing as a way of assessing whether their chosen outcomes were
indeed the best. Thus, in Study 2, we were particularly interested
in the relation between maximizing tendencies and social comparison, regret, and happiness with consumer purchasing decisions.
We anticipated that maximizing would predict reports of engaging
in more social comparison and experiencing greater regret in
general. In addition, we expected that maximizing would predict
reports of more product comparison, social comparison, and counterfactual thinking regarding purchases, and that these consumer
comparisons would lead to heightened consumer regret and decreased happiness regarding purchases.
Method
Participants, Materials, and Procedure
Participants were the 401 undergraduates described earlier as Sample 4
in Study 1. The materials comprised a questionnaire that included the
Maximization Scale, the Regret Scale, and the SHS from Study 1. In
addition, we created a scale to measure frequency of social comparison in
general, beliefs about the appropriateness of upward social comparison,
and beliefs about the appropriateness of downward social comparison.1 In
a pretest of this scale (n ⫽ 76), the subscales measuring frequency of social
comparison (␣ ⫽ .69), upward social comparison (␣ ⫽ .74), and downward
social comparison (␣ ⫽ .70) demonstrated adequate reliability. In addition,
this pretest demonstrated that the frequency of social comparison subscale
correlated with a validated measure of social comparison tendencies (r ⫽
.50, p ⬍ .001; Gibbons & Buunk, 1999).
Consumer behavior items were created regarding people’s general tendencies toward consumer-related social comparison, product comparison,
counterfactual thinking, and consumer regret. After completing these
items, participants were asked to recall either an expensive or an inexpensive recent purchase. Participants in the inexpensive condition were asked
to “recall the most recent item you have purchased that was relatively
inexpensive, say around $5.00. For example, the item might be a movie
rental, a book, or a magazine.” Those in the expensive condition were
asked to “recall the most recent item you have purchased that was relatively expensive, say around $500.00. For example, the item might be
sporting equipment, electronic equipment, etc.”
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All participants then answered specific consumer behavior questions
regarding their purchase, such as product price, product comparison, time
to decide on the product, prepurchase and postpurchase social comparison,
counterfactual thinking, happiness with the product, and regret regarding
the recalled purchase. The order of presentation of the Maximizing and
Regret Scales and the consumer behavior items was counterbalanced.
Because counterbalancing did not predict significant variance in any of the
dependent variables, the results are collapsed across this variable. Participants completed the questionnaire packet in class, and were debriefed at
the end of the study.
Results
Construction of Indexes
Composites of social comparison frequency (␣ ⫽ .68), downward social comparison (␣ ⫽ .68), and upward social comparison
(␣ ⫽ .71) were created. Composites also were constructed for
general product comparison (␣ ⫽ .72), general social comparison
(␣ ⫽ .72), and general consumer regret (␣ ⫽ .82). Finally, an
index of consumer maximizing tendencies for a specific purchase
was created by combining the measures of product comparison,
time to decide, prepurchase social comparison, postpurchase social
comparison, and counterfactual thinking (␣ ⫽ .81).
Happiness, Regret, and Social Comparison Tendencies
As reported in Study 1, maximizing was associated with being
less happy (though this relation was modest) and experiencing
more regret. We anticipated that maximizing also would predict
reports of engaging in social comparison. A linear regression
analysis with maximizing as the predictor on the overall index of
social comparison frequency supported this prediction, F(1,
393) ⫽ 39.07, p ⬍ .001, ␤ ⫽ .30. Regression analyses on the
upward and downward social comparison indexes indicated that
maximizing was also predictive of interest in upward, F(1,
397) ⫽ 8.99, p ⬍ .01, ␤ ⫽ .15, and downward, F(1, 397) ⫽ 21.14,
p ⬍ .001, ␤ ⫽ .23, social comparisons. In addition, maximizing
predicted reports of engaging in upward, F(1, 394) ⫽ 33.63, p ⬍
.001, ␤ ⫽ .28, and downward, F(1, 395) ⫽ 15.09, p ⬍ .001, ␤ ⫽
.19, social comparisons more frequently.
Regression analyses indicated that both frequency of downward
social comparison (when statistically controlling for frequency of
upward social comparison), F(2, 392) ⫽ 15.23, p ⬍ .001, ␤ ⫽ .18,
and frequency of upward social comparison (when statistically
controlling for frequency of downward social comparison), F(2,
392) ⫽ 42.19, p ⬍ .001, ␤ ⫽ .31, were predictive of reports of
1
In the interest of brevity, the items measuring social comparison
frequency (e.g., “How frequently do you compare yourself to other people
in general?”), appropriateness of upward social comparison (e.g., “Comparing oneself to those who are better off can be useful”), appropriateness
of downward social comparison (e.g., “It is inappropriate to compare one’s
own standing to those who are not doing as well” [reverse scored]), general
consumer behavior (e.g., for product comparison: “When I am planning to
purchase an item of clothing, I like to look at all the stores first to make
certain I get the perfect item”), and specific consumer behaviors (e.g., for
product comparison: “How many products did you consider before choosing this particular one?”) can be obtained from authors Katherine White
([email protected]) or Darrin R. Lehman (dlehman@cortex
.psych.ubc.ca).
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regret. In addition, although frequency of downward social comparison (when statistically controlling for frequency of upward
social comparison) was not predictive of subjective happiness,
F(2, 392) ⫽ 0.47, ns, ␤ ⫽ .04, upward social comparison frequency (controlling for downward social comparison) was, F(2,
392) ⫽ 10.09, p ⬍ .01, ␤ ⫽ ⫺.16.
Of interest, maximizers seemed to be oriented toward both
upward and downward social comparisons. Past research and
theorizing suggest that upward comparisons may trigger negative
affective states, lead to low ratings of subjective well-being, and
result in negative consequences for the self (e.g., Diener, 1984;
Morse & Gergen, 1970; Salovey & Rodin, 1984), whereas downward comparisons often have the opposite effect, allowing the
individual to feel better in comparison to a worse off other (e.g.,
Morse & Gergen, 1970; Wills, 1981). The puzzle here is that
although maximizing was predictive of engaging in more downward social comparison, it was also predictive of regret. Is it the
case that maximizers are susceptible to the negative consequences
of upward social comparison, but unable to reap the benefits of
downward social comparison? This is not implausible in light of
suggestive evidence that social comparison in general is not compatible with happiness (Lyubomirsky & Ross, 1997; Lyubomirsky,
Tucker, & Kasri, 2001).
To address this possibility, we examined whether upward social
comparison and downward social comparison were related to
regret among those high in maximization. We performed a median
split on the maximizing scale, and examined the relation between
regret and social comparison among those scoring high on the
maximizing scale. The results revealed that, among those high on
maximizing, frequency of upward social comparison (controlling
for downward social comparison) was predictive of regret, F(2,
197) ⫽ 7.08, p ⬍ .01, ␤ ⫽ .19, whereas frequency of downward
social comparison (controlling for upward social comparison) was
not, F(2, 197) ⫽ 2.50, p ⬍ .12, ␤ ⫽ .11. We also found that,
among those high on maximizing, frequency of upward social
comparison (controlling for downward social comparison) was
predictive of decreased happiness, F(2, 198) ⫽ 6.57, p ⬍ .02, ␤ ⫽
⫺.18, whereas frequency of downward social comparison (controlling for upward social comparison) was not positively related
to happiness, F(2, 198) ⫽ .25, ns, ␤ ⫽ .04. This provides some
support for the notion that whereas maximizers tend to experience
the negative consequences of upward social comparisons, they are
unable to benefit from downward social comparisons.
General Consumer Behaviors
Linear regression analyses on the general consumer behavior
items revealed that maximizing predicted the tendency to engage
in product comparison, F(1, 397) ⫽ 42.49, p ⬍ .001, ␤ ⫽ .31,
social comparison, F(1, 396) ⫽ 12.27, p ⬍ .01, ␤ ⫽ .17, and
counterfactual thinking, F(1, 397) ⫽ 29.40, p ⬍ .001, ␤ ⫽ .26,
regarding purchases. Further, maximizing was predictive of reports of consumer regret, F(1, 397) ⫽ 19.16, p ⬍ .001, ␤ ⫽ .22.
Consumer Behaviors for Recalled Purchases
Participants were asked to report on either an inexpensive or an
expensive purchase. The average amount spent on inexpensive
purchases was $6.57, and maximizing was not predictive of the
amount spent on inexpensive purchases (F ⬍ 1, ns). The majority
of inexpensive purchases were magazines (22.4%), movie rentals
(22.0%), food (15.1%), and books (10.7%). Other inexpensive
purchases included such things as cosmetics, school supplies, and
CDs. The average amount spent on expensive purchases was
$538.00, and, once again, maximizing was not predictive of the
amount spent (F ⬍ 1, ns). The majority of expensive purchases
were stereo equipment (16.8%), computers (15.8%), and clothing
(15.3%). Other expensive items included sporting equipment and
other electronic items (e.g., TVs, cell phones).
Regression analyses indicated that, when recalling a specific
purchase, maximizing predicted the consideration of more products, F(1, 389) ⫽ 5.23, p ⬍ .01, ␤ ⫽ .12, and taking longer to
decide, F(1, 390) ⫽ 13.13, p ⬍ .001, ␤ ⫽ .18. Maximizing
predicted reports of engaging in social comparison both before,
F(1, 390) ⫽ 4.51, p ⬍ .04, ␤ ⫽ .11, and after, F(1, 390) ⫽ 5.52,
p ⬍ .02, ␤ ⫽ .12, making purchases. Furthermore, maximizing
was associated with engaging in more counterfactual thinking
regarding purchases, F(1, 390) ⫽ 34.12, p ⬍ .001, ␤ ⫽ .28.
Finally, maximizing was predictive of reports of diminished positive feelings toward purchases (i.e., an index of happiness and
regret, with regret reverse scored), F(1, 389) ⫽ 9.68, p ⬍ .01, ␤ ⫽
⫺.16. Thus, it appears that maximizers not only report engaging in
more comparisons (i.e., product comparisons, social comparisons,
and counterfactual comparisons) regarding their consumer decisions, they also report experiencing heightened regret and decreased happiness.
Given the relation between maximizing and happiness observed
in these studies, it is possible that findings that we have attributed
to individual differences in maximizing may really be due to
differences in dispositional happiness, a plausible hypothesis given
Lyubomirsky and Ross’s (1997) finding that unhappy people are
more affected by upward social comparison information than
happy people. To examine this possibility, we conducted partial
correlation analyses between maximizing and regret, frequency of
social comparison, maximizing tendencies (i.e., an index of time to
decide on the purchase, product comparison, prepurchase social
comparison, postpurchase social comparison, and counterfactual
thinking), and consumer feelings (i.e., an index of consumer happiness and regret), controlling for dispositional happiness. The
partial correlations between maximizing and regret (r ⫽ .39, p ⬍
.001), frequency of social comparison (r ⫽ .27, p ⬍ .001), maximizing tendencies (r ⫽ .19, p ⬍ .001), and consumer feelings
(r ⫽ ⫺.13, p ⬍ .02) all remained significant when levels of
happiness were statistically controlled. Thus, it appears that maximizing makes a contribution to regret, to social comparison, to
consumer behaviors, and to consumer satisfaction over and above
that of dispositional happiness.
Discussion
As anticipated, maximizing was predictive of reports of engaging in social comparison, being concerned with what others were
doing, and finding upward and downward social comparison more
appropriate. Maximizing also predicted product comparison, social
comparison, and counterfactual thinking with regard to purchases.
Moreover, maximizing predicted consumer feelings, such that
those high on maximizing ultimately experienced more regret and
less happiness regarding their purchases. These patterns held after
controlling for dispositional happiness. Furthermore, our findings
regarding consumer behavior suggest that social comparisons and
product comparisons stimulated counterfactual thoughts, which
then engendered regret (see, e.g., Roese, 1997). Although a measure of general counterfactual thinking was not included in this
study, recent research indicates that maximizers ruminate more
than satisficers (White, Lehman, & Schwartz, 2002). It may be the
case that counterfactual thinking and ruminative thoughts are
related to the general regret reported by maximizers, as well as to
consumption-related regret. Thus, it appears that striving for the
best things in life may have paradoxical consequences.
Intuition, along with previous research (e.g., Morse & Gergen,
1970), suggests that whereas upward social comparison might
yield regret and unhappiness, downward social comparison might
yield elation. Study 2 found no such mood enhancing effects of
downward social comparison. However, a close look at the recent
literature on social comparison suggests that consistent positive
effects of downward social comparison are reliably reported only
for individuals who have low self-esteem or experience physical or
psychological threat (e.g., Affleck & Tennen, 1991; Aspinwall &
Taylor, 1993; Gibbons & Gerrard, 1989; Taylor, 1983; see Wills,
1991, for a review). In the general population, the mood effects of
social comparison are much less predictable. Recent findings suggest that the affective consequences of social comparison are not
intrinsic to its direction (e.g., Buunk, Collins, van Yperen, Taylor,
& Dakof, 1990). That is, both upward and downward comparisons
can have positive or negative implications for the self (e.g., Brewer
& Weber, 1994; Brown, Novick, Lord, & Richards, 1992; Buunk
et al., 1990; Hemphill & Lehman, 1991; Lockwood & Kunda,
1997; Taylor, Buunk, & Aspinwall, 1990; Tesser, 1988; Wood &
VanderZee, 1997).
A limitation of Study 2 is that although it relied on reports of
real-life experiences, these were merely recalled by participants.
Because participants’ recollections of the purchasing situation
could be biased or incomplete, it is important to assess social
comparison, happiness, and regret among maximizers and satisficers in other settings as well. Thus, Study 3 attempted to examine
reactions to social comparison information in a controlled laboratory setting.
Study 3. Maximizing, Satisficing, and Social Comparison
Because maximizers are continually chasing the best possible
option when making a decision, they try to gather and analyze all
of the information available to them. Information about one’s
relative standing with one’s peers—that is, social comparison
information—is likely to be an important source of information in
their decision-making process. Thus, maximizers are expected to
be more interested in social comparison feedback and more sensitive to such feedback than satisficers.
Accordingly, the primary hypothesis tested in Study 3 was that
the moods and self-evaluations of maximizers would be more
vulnerable or sensitive to unsolicited social comparison information than would those of satisficers. This study asked participants
to solve anagrams at whatever rate they were capable, but manipulated the ostensible performance of an undergraduate peer so that
participants experienced relative “success” (i.e., their peer performed worse than themselves) or relative “failure” (i.e., their peer
performed better than themselves). This paradigm was developed
1187
by Lyubomirsky and Ross (1997, Study 1), who found support for
a parallel prediction regarding chronically unhappy and happy
individuals. That is, in their study, self-rated unhappy students who
solved puzzles in the presence of a faster peer showed smaller
increases in mood and self-confidence and expressed greater
doubts about their own ability than those exposed to a slower peer.
Happy individuals, by contrast, did not exhibit this pattern of
sensitive responding to social comparison feedback.
Study 3 was characterized by several notable features. First, to
minimize possible experimental demand characteristics and suspicion on the part of participants, and to simulate typical “realworld” peer comparison contexts, social comparison information
was provided indirectly. That is, the experimenter never explicitly
offered any comparison of performances, although such information was made highly salient to the participants. Second, the
relevant task and dimension of evaluation (i.e., anagram-solving
ability) was one about which participants were unlikely to have
objective standards for evaluating their performance. Finally, participants enjoyed wide latitude in managing the social comparison
information they faced. That is, they were free to minimize or
maximize the relevance, importance, and controllability of the
evaluation dimension; they were free to compete with, identify
with, or simply ignore their more or less successful peer; and they
were free to attribute their own performance and/or that of their
peer to whatever factors they wished.
To summarize, whereas in Study 2, participants only responded
to questions regarding their social comparison tendencies in connection with consumer choices, Study 3 used a more powerful and
more direct manipulation of social comparison information, one
involving a real-life peer performing alongside the participant in
the laboratory. And in Study 3, rather than measuring participants’
interest in and seeking of such comparison information, we examined the actual effects of social comparison information provided
by the context.
Method
Overview
In the context of a purported study of cognitive performance, maximizers and satisficers (as categorized by their earlier responses to the Maximization Scale) solved anagram puzzles while a supposed peer (who was
actually an experimental confederate) ostensibly completed the same set of
anagrams much faster or much slower than themselves. Participants rated
themselves with respect to their current mood and anagram-solving ability
both before and after completion of the anagram-solving task.
Participants
Fifty-four students enrolled in an introductory psychology course at the
University of California, Riverside received course credit for their participation in this study. Participants were selected on the basis of their
responses to the 13-item Maximization Scale, which was presented in the
context of a mass-administered questionnaire (n ⫽ 82). Responses to the 13
items, which displayed good internal consistency (␣ ⫽ .79), were combined and averaged to provide a single composite score, ranging from 2.6
to 6.7, with a median of 4.2 on the 7-point scale.
A sample of 26 maximizers and 28 satisficers, that is, those whose
composite scores were respectively either in the top or bottom third of the
distribution, were recruited for the study by telephone. The maximizers’
group mean on the Maximization Scale was 5.26 (SD ⫽ 0.50), whereas the
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SCHWARTZ ET AL.
satisficers’ group mean was 3.49 (SD ⫽ 0.43). We should note that the
omnibus questionnaire used in selecting these participants also included the
SHS and BDI (Beck, 1967). The correlations between participants’ scores
on the Maximization Scale and their scores on the SHS and BDI were
moderate to high (r ⫽ ⫺.27 and r ⫽ .46, respectively). The inclusion of
these scales, although not specifically intended for this purpose, allowed us
to pursue issues of discriminant validity.
Procedure and Materials
In each experimental session, two individuals—a participant and a
same-gender confederate pretending to be another participant— completed
the relevant questionnaires and experimental tasks together. The experimenter, who was unaware of participants’ maximization status, explained
that participants were being paired simply to “save time.”
The experiment was introduced as a study of “cognitive performance”—
that is, one in which “we hoped to learn how personality and various
situational variables affect performance on a problem-solving task.” Accordingly, participants were told they would be asked to solve a series of
anagram puzzles during the experimental session. To bolster this cover
story, a number of filler items, including questions about how often
participants solved puzzles and how much they enjoyed them, as well as
their quantitative and verbal Scholastic Assessment Test (SAT) scores,
were embedded in the various questionnaires administered throughout the
study.
Before undertaking the primary experimental task, participants completed a preliminary questionnaire assessing their premanipulation or
“baseline” mood. Mood was assessed with the Positive and Negative
Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988), with 10
items measuring positive affect (PA; ␣ ⫽ .87) and 10 items measuring
negative affect (NA; ␣ ⫽ .79) on 5-point Likert-type scales. Participants
also provided a baseline measure assessing how good they initially thought
they were at solving anagrams (1 ⫽ very poor, 7 ⫽ excellent).
Anagram-solving task and social comparison manipulation. After the
participants had completed the preliminary questionnaire, a female experimenter gave instructions for the 15-min anagram-solving task, which
closely followed a procedure developed by Lyubomirsky and Ross (1997,
Study 1). She began by handing each of them a “sample” puzzle card
containing three anagrams—that is, Y-O-W-N-S (SNOWY), N-O-T-I-X
(TOXIN), and A-S-S-I-B (BASIS)—and indicating that such cards would be
used throughout the anagram task. She further explained that upon unscrambling any two of the three anagrams on a given card, they were to
write their solutions and the card number on their answer sheet, then hand
the card back to the experimenter and receive a new card containing new
anagrams. Participants were also given a notebook to use as scratch paper
(one page per card). At that point, the experimenter instructed them to
begin solving anagrams and handing in their cards. What the participants
did not know was that their coparticipant was an experimental confederate
who had been instructed to monitor their partner’s pace and solve either
approximately twice as many or half as many anagrams as he or she did—a
task that they accomplished successfully. The back-and-forth handing of
the anagram cards as the participant and confederate worked side-by-side
throughout the 15-min period, along with the consecutive numbering of the
cards and the turning of notebook pages, served to make it highly salient
to participants that their peer was performing at a much faster or much
slower pace.
Postperformance questionnaires. Immediately following the 15-min
anagram task, the participant and confederate were led to separate rooms,
and the participant was asked by the experimenter to complete a second set
of self-assessments. To assess participants’ changes in mood as a function
of their own performance in the two social comparison conditions, participants were asked to complete the PANAS for a second time (␣ ⫽ .92 and
␣ ⫽ .83 for the PA and NA scales, respectively). To assess changes in
participants’ perceptions of their own ability in light of their performance
and the apparently superior or inferior performance of their peer, participants were asked to rate again how good they thought they were at solving
anagrams (1 ⫽ very poor, 7 ⫽ excellent). As a manipulation check,
participants were next asked to rate their own just-completed task performance and that of the confederate using 7-point scales (1 ⫽ very poor, 7 ⫽
excellent).
Finally, when all dependent variables had been collected, participants
completed a debriefing questionnaire and engaged in an oral debriefing, in
which they were given an opportunity to share their hunches about the
hypothesis of the study and to report any other suspicions. No guesses or
notable suspicions were reported. The entire session, including a process
debriefing (Ross, Lepper, & Hubbard, 1975), lasted approximately 1 hr.
Results
Premanipulation Measures
Measures completed prior to the anagram-solving task suggested no between-group differences in ability or experience. That
is, maximizers and satisficers did not differ significantly in their
quantitative and verbal SAT scores, in their initial self-ratings of
ability at solving anagrams, in their reports of how often they
solved anagrams, or in how much they enjoyed solving them (all
ts ⬍ 1). Notably, the two groups also did not differ significantly in
their baseline moods (ts for both PA and NA ⬍ 1).
Manipulation Check
Overall, participants solved a mean of 12.7 anagrams
(SD ⫽ 7.84) during the allotted 15-min test period. Analyses
revealed no significant performance differences between satisficers and maximizers overall, t(53) ⫽ 1, or in either the “faster
peer” (t ⬍ 2) or “slower peer” (t ⬍ 1) conditions. Nor was any
main effect found for peer performance on the participants’ own
performance, t(43) ⫽ 0.74, ns. As instructed, the confederate,
depending on experimental condition, “solved” either considerably
more anagrams (M ⫽ 27.29; SD ⫽ 8.53) or considerably fewer
anagrams (M ⫽ 8.01; SD ⫽ 5.90) than the participant. The
participants, moreover, showed themselves to be well aware of
these performance differences. Those in the “faster peer” condition
rated their peer as significantly better at solving anagrams
(M ⫽ 6.07, SD ⫽ 0.94) than did those in the “slower peer”
condition (M ⫽ 3.08, SD ⫽ 0.93), t(51) ⫽ 11.73, p ⬍ .001.
Finally, participants who witnessed a faster peer rated themselves
as significantly worse at solving anagrams (M ⫽ 2.29, SD ⫽ 1.15)
than did those who witnessed a slower peer (M ⫽ 3.81,
SD ⫽ 1.52), t(46) ⫽ 4.12, p ⬍ .001.
Strategies for Statistical Analyses
We hypothesized that maximizers’ moods and self-assessments
of ability would depend heavily on the quality of their peer’s
performance—that is, whether it was inferior or superior to their
own. Satisficers’ moods and self-assessments, by contrast, were
expected to be less influenced by the performance of their peer.
We conducted planned pairwise comparisons of the faster peer and
slower peer conditions within the two groups (Rosenthal & Rosnow, 1985; see also Rosnow & Rosenthal, 1989, 1995). Additionally, we compared maximizers who had witnessed a faster peer
with those in the three remaining conditions. Simple change scores
reflecting differences between premanipulation and postmanipula-
tion ratings provided the primary dependent variables for both
types of analyses. Other types of statistical analyses, such as
analyses of covariance (ANCOVAs) and repeated measures analyses, were also performed and yielded results very similar to those
obtained in the analyses reported in this article. For brevity, these
results are not discussed.
1189
assessments of the four groups, F(1, 50) ⫽ 11.33, r ⫽ .43, p ⬍
.002, but their mean decline was the only one of the four that was
significantly different from zero (M ⫽ ⫺1.71), t(14) ⫽ 5.06, p ⬍
.001. However, differences between the responses of maximizers
and satisficers in this condition—at least in terms of changes in
their self-assessments of ability— did not reach conventional levels of statistical significance, F(1, 50) ⫽ 2.73, p ⬍ .10.
Changes in Self-Assessments of Ability
We expected maximizers to offer ratings of their own ability
that gave considerable weight to social comparison information.
Supporting this prediction, a planned contrast revealed that maximizers gave lower assessments of ability on the anagram task after
working alongside a faster peer (M ⫽ ⫺1.71, SD ⫽ 1.27) than
after working alongside a slower peer (M ⫽ 0.17, SD ⫽ 1.11), F(1,
50) ⫽ 10.34, p ⬍ .003. Self-assessments of satisficers, by contrast,
did not differ significantly between the two social comparison
conditions (Ms ⫽ ⫺0.79 vs. 0.14), F(1, 50) ⫽ 2.63. The top panel
of Figure 1, as well as the top of Table 3, shows the mean changes
in self-assessments of ability, based on 7-point rating scales, for all
four groups.
As expected, maximizing participants in the “faster peer” condition not only exhibited the largest relative decline in self-
Changes in Self-Reported Affect
Examination of changes in participants’ self-reported negative
mood (NA) produced a similar pattern of results (see the bottom
panel of Figure 1 and the top of Table 4). Once again, supporting
our predictions, maximizers displayed significantly more elevated
NA after witnessing a faster peer (M ⫽ 0.54, SD ⫽ 0.82) than after
witnessing a slower peer (M ⫽ ⫺0.03, SD ⫽ 0.50), F(1, 50) ⫽
6.31, p ⬍ .02. Satisficers, by contrast, showed more similar affect
in response to their superior versus inferior peer’s performance
(Ms ⫽ 0.12 vs. ⫺0.06), F ⬍ 1.
Following the pattern of results for self-assessments, maximizers in the “faster peer” condition not only exhibited the biggest
increase in NA of the four groups, F(1, 50) ⫽ 8.70, p ⬍ .005, but,
once again, their mean increase was the only one of the four groups
Figure 1. Changes in assessments of ability (top) and negative affect (bottom) after working alongside a slower
versus faster peer (Study 3).
SCHWARTZ ET AL.
1190
Table 3
Maximization Versus Happiness and Dysphoria in Determining
Changes in Participants’ Self-Assessments of Ability
in Response to a Faster Versus Slower Peer
Group
Condition
Faster peer
Slower peer
Difference
Maximizers
Raw means
⫺1.71
⫺0.17
1.88
Satisficers
⫺0.79
0.14
0.93
Adjusted means (with SHS score as covariate)
Faster peer
Slower peer
Difference
⫺1.98
⫺0.16
1.82
⫺0.57
⫺0.47
1.04
Adjusted means (with BDI score as covariate)
Faster peer
Slower peer
Difference
⫺1.88
0.00
1.88
⫺0.65
0.32
0.97
Note. SHS ⫽ Subjective Happiness Scale; BDI ⫽ Beck Depression
Inventory.
that was significantly different from zero (M ⫽ 0.54),
t(14) ⫽ 2.43, p ⬍ .03. Furthermore, differences between the
responses of maximizers and satisficers in this condition were
marginally statistically significant, F(1, 50) ⫽ 3.62, p ⬍ .07. None
of the analyses examining group differences in changes in participants’ positive mood (PA) reached conventional levels of statistical significance.
Subjective Happiness and Dysphoria as Possible
Moderator Variables
Readers might question whether the effects reported thus far
really reflect the role of a maximization orientation rather than that
of chronic happiness or dysphoria. Indeed, similar effects have
been reported in an analysis of dispositionally happy and unhappy
individuals (Lyubomirsky & Ross, 1997, Study 1). Given the
moderately high correlations in this study between participants’
scores on the Maximization Scale and their scores on the SHS and
the BDI, it was deemed prudent to address this question through
covariance analyses.
With respect to both happiness (as measured by the SHS) and
dysphoria (as measured by the BDI), the results of our analyses
were simple and conclusive. When either happiness or dysphoria
was introduced as a covariate, neither variable accounted for our
between-group differences. That is, both for changes in selfassessment of ability and for changes in NA, ANCOVAs left our
“adjusted” means for both ability change (see Table 3) and NA
change (see Table 4), as well as the relevant contrasts, virtually
unaltered in magnitude.
Discussion
The results of Study 3 supported our hypothesis that maximizers
would be more affected by social comparison information than
would satisficers. Maximizers who saw their peer solve anagrams
faster than themselves expressed greater doubts about their own
ability at the task and displayed a greater increase in negative
mood than maximizers who saw their peer solve fewer anagrams.
Satisficers, by contrast, showed little or no such response to the
social comparison information provided by their peer. Subsequent
analyses, moreover, suggested that it was differences in maximization per se, rather than the association of maximization with
happiness or dysphoria, that predicted the relevant differences in
response.
Although the focus of this study was on the ways that students
use self-relevant social comparison feedback when evaluating
themselves, the relevant social comparison processes are likely to
mirror those recruited in decision-making contexts of the sort
investigated in Study 2. We suspect that many decisions faced by
students—for example, which major to choose, to which graduate
school to apply, which job to select—involve self-evaluations, and
comparisons with peers can provide feedback about whether one
can “cut it” in a particular major, school, or job. We speculate that
because satisficers are satisfied with a major, school, or job that is
simply “good enough,” they may not require as much information
in general—and social comparison information in particular—as
do maximizers in order to make decisions.
Several issues raised by Study 3 deserve comment. First, given
that changes in participants’ PA did not show the expected pattern
of results, further research could test the possibility that it is on NA
that social comparison has its major influence. Second, because the
current study examined differences in maximizers’ and satisficers’
responses to social comparison information and not their interest in
or seeking of such information, the latter topics remain worthy of
investigation (though the questionnaire responses in Study 2 suggest that maximizers seek more social comparison information
than satisficers). And, finally, because Study 3 was conducted in
Table 4
Maximization Versus Happiness and Dysphoria in Determining
Changes in Participants’ Self-Reported Negative Affect in
Response to a Faster Versus Slower Peer
Group
Condition
Maximizers
Satisficers
Raw means
Faster peer
Slower peer
Difference
0.54
⫺0.03
0.57
0.12
⫺0.06
0.18
Adjusted means (with SHS score as covariate)
Faster peer
Slower peer
Difference
0.62
0.06
0.56
0.06
⫺0.16
0.22
Adjusted means (with BDI score as covariate)
Faster peer
Slower peer
Difference
0.63
0.07
0.56
0.04
⫺0.17
0.21
Note. SHS ⫽ Subjective Happiness Scale; BDI ⫽ Beck Depression
Inventory.
the laboratory, the question remains how maximizers and satisficers respond to social comparison feedback during the course of
decisions in their everyday life.
Taken together, our findings in Studies 2 and 3 provide support
for the notion that maximizers are more likely than satisficers to
seek out and respond to social comparison information each time
they try to make the “perfect choice.”
Study 4. Maximizing, Satisficing, and Regret
The first three studies have provided evidence of individual
differences in the disposition to maximize that correlate with other
important variables and are reflected in self-reports about purchasing decisions. Further, there is evidence, both from self-report and
experimental data, that maximizers are more inclined to engage in
social comparisons and to be more sensitive to their contents than
are satisficers. The final study reported here tested whether a
disposition to maximize relates to actual decision-making behavior. We created a game that required participants to make decisions, and investigated whether maximizers made different decisions, and experienced different degrees of satisfaction from those
decisions, than did satisficers.
The second aim of Study 4 was to explore experimentally the
relation between maximizing and regret. We reported in Study 1
consistent and substantial correlations between scores on our Maximization Scale and scores on our Regret Scale. We also reported
evidence that partially supported the hypothesis that regret mediates the relations between maximization and various measures of
well-being. On the basis of this evidence, we suggested that one of
the factors that may lead maximizers to experience less happiness
and satisfaction with life than satisficers is maximizers’ increased
sensitivity to regret— both experienced and anticipated. If that is
true, then it should be the case that experimental manipulations
designed to enhance the possibility of experiencing regret should
have a larger impact on maximizers. The game used in Study 4 was
designed to manipulate the potential to experience regret.
We used a variant of the ultimatum game (Camerer & Thaler,
1995; Guth, Schmittberger, & Schwarze, 1982). In the ultimatum
game, one player has control of a resource (typically a sum of
money) and offers some part of that resource to another player.
That player may either accept the offer, in which case the resource
is divided in keeping with the offer, or reject it, in which case
neither player gets anything. This game has been of interest to
experimental economists because an analysis of optimal strategy
by a rational maximizer of gain would seem to dictate that the
proposer make the smallest legal offer, secure in the knowledge
that the recipient of that offer will accept it (a little of something
is better than nothing). This pattern is virtually never observed
among actual participants. First, recipients of offers routinely
reject them if they are too low (e.g., less than 30% of the resource).
Second, proposers rarely make such low offers.
With respect to regret, there is an interesting asymmetry to the
ultimatum game. The proposer will always know if he or she has
made an offer that is too low, because the recipient of that offer
will have rejected it. However, the proposer will not know if the
offer was too high because there is no information about the
minimum acceptable offer—the reservation price. When the recipient accepts the offer, it could be that the offer was at exactly the
price necessary for acceptance or that it was higher than necessary.
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Thus, one would expect proposers who are worried about regretting their decisions to make unnecessarily high offers. That way,
they will avoid the only source of regret that the situation permits—an offer that is rejected. Suppose, however, that the game
were altered so that proposers would be told what the minimum
acceptable offer was on trials of the game in which their offers
were accepted. Thus, they might offer $5 of a $10 stake, have their
offer accepted, and then find out that an offer of $3 also would
have been accepted. Under these conditions, it is possible to regret
offers that are too high just as it is possible to regret offers that are
too low. Zeelenberg and Beattie (1997) found that offers in this
modified ultimatum game tended to be lower than offers under the
standard procedure. Our question, based on the hypothesis that
maximizers are more sensitive to regret than satisficers, was
whether the effect observed by Zeelenberg and Beattie would be
larger for maximizers than for satisficers.
Method
Participants
The participants were 84 students (48 female and 36 male) enrolled in an
introductory psychology course at Swarthmore College who received
course credit.
Procedure
All participants had previously completed a packet of questionnaire
materials including the Maximization Scale and the Regret Scale. Approximately 7 weeks later, participants were directed to a Web site for participation in another study. They were given 2 weeks in which to do the tasks
on the Web site at a time and place that was convenient to them. About
75% of the participants completed the tasks within the allotted time. The
others were sent follow-up reminders by e-mail until all but 7 had complied. No mention was made of the connection between this study and the
questionnaire materials they had completed earlier.
Each participant played two versions of the ultimatum game, in counterbalanced order: a “standard” version and a modified version (they
differed in only one respect, described below). Each version included 10
rounds. In the standard version, participants first encountered a screen that
told them that they were “Player 1,” that the computer would be “Player 2,”
and that the computer would be making decisions based on the performance of real people playing the identical game. Participants were also told
that on each round, they would be given a sum of money (between $8 and
$15). They were to make a whole dollar offer to Player 2 (the computer),
who would know what amount of money was being divided on each round,
and could accept or reject the offer. Participants were further informed that
the computer would simulate Player 2’s responses on the basis of past
behavior of people who have played this game. Moreover, it was explained,
on each round, a different past player would be used for the simulation, so
participants were to treat each round as playing with a different Player 2.
For each round, if the participant’s offer was accepted, Player 2 (the
computer) would “get” the amount offered, while Player 1 (the participant)
would get the difference between the total amount and the amount offered.
Thus, for example, if a round started with $12 available, and Player 1 made
an offer of $5 that was accepted, Player 2 would get the $5, and Player 1
would get $7. If the offer was rejected, neither player would get anything.
Participants were also told that there was a chance that they would actually
get to keep whatever amount resulted from a given round of the game. At
the end of each round, participants were asked to click the mouse along an
unmarked line that was anchored on the left with very unsatisfied and on
the right with very satisfied to indicate their satisfaction with that round of
the game.
SCHWARTZ ET AL.
1192
In the modified version, to which all participants also were exposed, at
the end of a round, in addition to being told whether their offer was
accepted or rejected, they were also told what the smallest offer was that
Player 2 would have accepted—Player 2’s “reservation price.” The reservation price of Player 2 was programmed to vary pseudorandomly, with a
low of 13% of the initial sum and a high of 57% of the initial sum. The idea
behind this manipulation was that in the standard game, players never
know that they have offered more than was necessary, and thus will not
experience regret over offers that are too high. In this variant of the game,
participants would know when they had made offers that were more
generous than necessary.
Results and Discussion
Participants offered their counterpart half of the initial sum of
money (rounded to the nearest dollar) on 53.4% of trials, less than
half on 37.3% of trials, and more than half on 9.3% of trials. Given
the low rates of offers above half, the data were collapsed into
offers of less than half (37.3%) and offers of at least half (62.7%).
No significant difference in rates of offering less than half was
found between males and females (42.2% vs. 33.4% for males vs.
females, respectively; t ⬍ 1.5). Maximization score was not correlated with the percentage of offers made below half (r ⫽ .15,
n ⫽ 82, ns). However, an ANCOVA (with maximization score as
a continuous variable) revealed that there was a significant interaction between gender and maximization score on the number of
offers made of less than half, F(1, 82) ⫽ 6.80, p ⬍ .01. The
relation between maximization score and offers made was thus
analyzed separately for males and females. Among males, participants higher in maximization exhibited a significantly higher
percentage of offers below half (r ⫽ ⫺.40, n ⫽ 35, p ⬍ .02).
Among female participants, no significant association was found
between maximization score and the percentage of offers below
half (r ⫽ .14, n ⫽ 47, ns).
Within-participant t tests comparing offers made on trials in
which the reservation price of opponents was shown versus those
in which it was not shown did not indicate any difference in the
percentage of offers of less than half (38.4% vs. 36.0%; t ⬍ 1).
Thus, we failed to replicate the findings of Zeelenberg and Beattie
(1997) for the participants as a group. However, experimental
condition did interact with maximization scores in predicting the
number of offers made of less than half by each participant. Using
a repeated measures analysis of variance (ANOVA), with experimental condition (i.e., whether or not reservation prices were
shown) as the repeated factor, there was a significant Condition ⫻
Maximization interaction in the percentage of offers made below
half, F(1, 80) ⫽ 8.90, p ⬍ .004. That is to say, satisficers and
maximizers tended to adjust their offers differently on the basis of
whether reservation prices were shown.
To determine the basis of the interaction between experimental
condition and maximization score on offers made, participants
were divided into satisficers and maximizers on the basis of a
median split. Among satisficers, participants exhibited lower rates
of offers of less than half on rounds in which reservation prices
were seen as compared with those in which reservation prices were
not seen (32.2% vs. 39.4%, respectively), t(40) ⫽ 2.42, p ⬍ .02—a
surprising result given what Zeelenberg and Beattie (1997) found.
Maximizers, however, did the opposite, confirming our expectations. They revealed higher rates of offering less than half on trials
in which reservation prices were seen as compared with those in
which reservation prices were not seen (48.1% vs. 35.9%, respectively), t(40) ⫽ 3.42, p ⬍ .001.
As would be expected, mean ratings of satisfaction were considerably higher on rounds in which the participants’ offer was
accepted (M ⫽ 7.02) than on those in which it was rejected
(M ⫽ 3.18), t(80) ⫽ 13.40, p ⬍ .001. Controlling for whether
offers were accepted, higher maximization scores predicted lower
judgments of satisfaction, F(1, 81) ⫽ 7.60, p ⬍ .01. Thus, being a
maximizer seemed to mean being less satisfied with the results of
an episode, independent of what those results were. Judgments of
satisfaction did not differ by condition (5.88 vs. 5.97, for standard
vs. modified version, respectively; t ⬍ 1). Further, no interaction
was present between condition and maximization scores in predicting judgments of satisfaction (F ⬍ 1).
On the basis of a repeated measure ANOVA, there was a trend
suggesting an interaction between maximization score and the
acceptance of offers as predictors of judgments of satisfaction,
F(1, 74) ⫽ 2.60, p ⬍ .11. To explore this trend, the associations
between maximization score and ratings of satisfaction were assessed separately for those rounds in which offers were rejected
and those in which offers were accepted. No significant correlation
was found between maximization score and judgments on those
trials in which offers were rejected (r ⫽ ⫺.07, n ⫽ 77, ns), but
participants higher in maximization were relatively less satisfied
during rounds in which offers were accepted (r ⫽ ⫺.31, n ⫽ 82,
p ⬍ .005). This negative correlation was present both on rounds in
which reservation prices were shown (r ⫽ ⫺.30, n ⫽ 82, p ⬍
.006), and those in which reservation prices were not shown (r ⫽
⫺.28, n ⫽ 81, p ⬍ .02).
To summarize, the results of Study 4 were consistent with many,
but not all, of our predictions. In the ultimatum game, male (but
not female) maximizers made smaller offers than male satisficers.
Maximizers of both genders offered less when the recipient’s
reservation price was going to be revealed (as we expected), but
unexpectedly, satisficers offered more when the recipient’s reservation price was going to be revealed. Finally, as hypothesized,
maximizers were less satisfied than satisficers with outcomes
generally. However, they were not especially dissatisfied in the
condition in which reservation price was revealed, as had been
expected.
The observed interaction on offers made between Maximization
scores and gender may reflect the presence of implicit social
payoffs present in the task. The presumed incentive to make lower
offers when reservation prices are revealed rests on the expectation
that finding out that larger gains could have been made will invite
regret. To the extent that participants experience the game as a
social interaction, however, this may not be the case. Most obviously, motivations of cooperation and fairness may result in the
experience of maximal utility with an even split as opposed to one
in which the participant gets more than half the money. However,
the social motive of competitiveness might result in higher utility
for the more financially favorable split. So it is possible that the
fact that males, but not females, tended to make lower offers when
reservation prices were revealed may reflect a greater display of
social motivation toward cooperation and fairness among females
and/or greater social motivation toward competitiveness among
males. Indeed, it is further possible that, particularly in a situation
with little truly at stake, the presence of feedback indicating that
the possibility to exploit was present but not taken (the condition
with known reservation prices) could make the choice of an
equitable split even more rewarding to participants with social
motives favoring equity. If so, this might explain the tendency of
satisficers to offer more 50 –50 splits in the condition in which
reservation prices were revealed than when they were hidden.
Finally, the fact that maximizers were not particularly dissatisfied with the condition in which reservation prices were revealed
(thus inviting more regret) might have been due to the above
reported interaction between condition and maximizing score on
offers made. Maximizers tended to make lower offers in the
condition in which reservation prices were shown, which may have
effectively offset the hypothesized increased tendency of this condition to invite regret. Consistent with this interpretation, maximizers’ lower offers led to obtaining more than half the available
money on 20.0% of trials in the condition in which the reservation
prices were shown as compared with 12.0% of trials when reservation prices were hidden. Satisficers’ rates for such gains
were 10.1% and 13.2%, respectively.
General Discussion
The present studies provide evidence for individual differences
in the orientation to seek to maximize one’s outcomes in choice
situations. Study 1 reported data with two new scales, a Maximization Scale and a Regret Scale, designed to measure individual
differences in maximization as a goal and in sensitivity to regret.
With seven independent samples, we found significant positive
correlations between maximization and regret, perfectionism, and
depression, and significant negative correlations between maximization and happiness, optimism, satisfaction with life, and selfesteem. We suggested that maximizers may be more concerned
with relative position, and thus more inclined to engage in social
comparison, than satisficers.
We explored this possible relation between maximizing and
social comparison in Studies 2 and 3. In Study 2, we found that
maximizers were more likely than satisficers to report engaging in
social comparison, both in general and in connection with consumer decisions. We also found that maximizers were more regretful and less happy with their consumer decisions than satisficers. In Study 3, we found a tendency for maximizers to be
affected by social comparison, this time in an experimental setting
in which the opportunities to compare oneself with others had
effects on assessments of task ability and on mood for maximizers
but not for satisficers. Finally, in Study 4, we found that maximizers were less satisfied than satisficers with their results in an
ultimatum bargaining game, and we obtained partial support for
the hypothesis that maximizers are more sensitive to regret than
satisficers.
Wieczorkowska and Burnstein (1999) recently reported evidence for an individual difference variable related to our distinction between maximizing and satisficing. They distinguished between individuals who have “point” search strategies and those
who have “interval” strategies in making decisions. For the former
group, the set of acceptable options is narrow, whereas for the
latter it is broad. The broad “interval” strategy is adaptive when
search costs are high or environmental opportunities are scarce.
The “point” strategy is adaptive when search costs are low or
opportunities are plentiful. Adaptive choosers are those who can
adjust their search strategies in keeping with what the environment
1193
makes available. This distinction between point and interval strategies is somewhat similar to the distinction between maximizing
and satisficing, but there is at least one important difference. The
point strategist differs from the interval strategist in having more
stringent standards of acceptability. However, those standards are
clear and explicit. The maximizer, in contrast, aspires to the (more
amorphous) “best.” Even in an abundant environment (indeed,
perhaps, especially in an abundant environment), finding the
“best” will always be difficult.
Taken together, our studies suggest that although maximizers
may in general achieve better objective outcomes than satisficers
(as a result of their high standards and exhaustive search and
decision procedures), they are likely to experience these outcomes
as worse subjectively. In what follows, we explore some of the
reasons why this may be so.
First, to be a maximizer is to want the best option. That, in turn,
requires an exhaustive search of the possibilities. Such a search is
hardly possible in any particular domain, and certainly impossible
in all domains. What this may mean to a maximizer is that when
practical constraints make exhaustive search impossible, there will
be anticipated regret about options foregone that might have been
better than the chosen option. There may also be experienced
regret at the chosen option because the chosen option, though the
best of all considered, was not necessarily the best in all respects.
That is, other options that may have been inferior overall may have
been better than the chosen option on one or more dimensions.
Such regret, whether caused by experienced or imagined alternatives, is sure to reduce the satisfaction derived from one’s choice.
In this connection, we wonder whether maximizers would be less
likely than satisficers to engage in dissonance reduction, for example, in a forced-choice paradigm.
Second, the process of adaptation will make virtually every
consumption experience less satisfying than one expects it to be
(e.g., Brickman & Campbell, 1971; Frederick & Loewenstein,
1999; Kahneman, 1999). What makes the adaptation process even
worse is that people tend not to anticipate it and thus mispredict
their future feelings about all sorts of experiences (Gilbert, Pinel,
Wilson, Blumberg, & Wheatley, 1998; Loewenstein & Schkade,
1999). When the experiences are positive, failure to make allowances for adaptation will make these experiences disappointing,
especially to maximizers, because their expectations will be higher
than those of satisficers (see below).
A particularly relevant example of people’s misprediction of
future subjective states was recently reported by Gilbert and Ebert
(2002), who conducted a series of studies in which participants
made a choice that was either reversible or not. Though they never
actually did reverse their choices, participants greatly preferred
being able to do so to having their decisions be final. Tellingly,
participants who had this decision-reversal option were actually
less satisfied with the outcomes of their decisions than those whose
decisions were irreversible. Gilbert and Ebert argued that when a
decision is final, various psychological processes get recruited
(e.g., dissonance reduction, rationalization) that subjectively improve the chosen alternative and denigrate the rejected one. As a
result of these processes, people experience enhanced satisfaction
with their decision. When people keep the option of decision
reversal, however, these psychological processes are not recruited.
Though the research has yet to be done, we anticipate that maxi-
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mizers would be much more inclined to desire to keep options
open than would satisficers.
The effects of adaptation may be worse for maximizers than
satisficers for two reasons. In all likelihood, maximizers have
higher standards of acceptability than satisficers, so that adaptation
is more likely to be disappointing. Also, it seems likely that the
decisions of maximizers entail far greater search costs than the
decisions of satisficers. If we imagine that these search costs get
“amortized” over the period of time in which the consequences of
the decision are positive, maximizers have a bigger “debt” to
amortize than satisficers.
Third, a maximizer is more likely to depend on social comparison than a satisficer. The truth of this claim seems inherent in the
logic of the matter. How does a maximizer decide that he or she
has attained the best possible outcome? Surely, in part, this decision is shaped by observing the outcomes of others. This logic is
buttressed by the evidence from Studies 2 and 3 that indicates that
maximizers do in fact engage more in social comparison, and are
more affected by it, than satisficers.
Fourth, it is plausible that maximizers have higher expectations
than satisficers. Given the practical constraints on search and
the adaptation processes already mentioned, excessively high
expectations are more likely to be met with disappointment. To
the extent that subjective well-being is in significant part a function of the relation between expectations and reality, as seems
likely, maximizers will often find that relation unsatisfying and
unsatisfactory.
The foregoing discussion helps explain why being a maximizer
may make one less happy, but what about the relation between
maximizing and depression? Schwartz (2000), in discussing the
choice problem, offered a speculative account of the increase in
clinical depression over the course of the last century. Such an
increase is surprising, because evidence suggests that having control over what happens is a key to avoiding depression (e.g.,
Abramson, Seligman, & Teasdale, 1978; Peterson & Seligman,
1984; Peterson, Maier, & Seligman, 1993; Seligman, 1975), and it
appears self-evident that adding options enhances one’s potential
control. The data reported in Study 1 seem to support Schwartz’s
speculation, at least for maximizers. But why? We believe that if
there is a causal link between being a maximizer and depression,
there is a key mediating factor—the presence of an overwhelming
array of options.
Our argument is as follows: the theory of depression based upon
the phenomenon of learned helplessness suggests that depression
results from a lack of control over significant events, coupled with
a particular attributional style for explanations of this lack of
control (Abramson et al., 1978). Consider the kinds of attributions
people might make when decisions lead to disappointing results.
Who is to blame? Is it the decision maker or the world? In a world
in which the options are few, it is reasonable to think that people
will blame the world for disappointing results. But in a world in
which the options are many, people will blame themselves. Thus,
we imagine that maximizing (in triggering disappointment) and a
proliferation of options (in triggering self-blame for disappointment) will interact to produce internal causal attributions for
failure on the part of maximizers. The proliferation of options has
two consequences related to this theory of depression. First, it
raises people’s standards for determining what counts as a success.
From breakfast cereals to automobiles to colleges to careers, it
makes sense for people to expect more when the options are
plentiful than when they are scarce. Second, failure to meet those
standards in a domain containing multiple options encourages one
to treat failures as the result of personal shortcomings rather than
situational limitations, thus encouraging a causal attribution for
failure that we might call “depressogenic.” So, in a world of
limited options, a maximizer might be more disappointed than a
satisficer with the results of his or her decisions without taking
personal responsibility for the disappointing results. But in a world
of limitless options, there is simply no excuse for failure.
What the above argument suggests is that it is a mistake to
equate choice with a sense of control, so that the more one has of
the former, the more one has of the latter. The relation between
choice and perceived control may, for various reasons already
discussed, be nonmonotonic (see Iyengar & Lepper, 2000). It may
be that what is often critical about control in preventing or alleviating depression is having a choice, not having many choices. For
example, a woman may be depressed because she feels she cannot
get out of a bad relationship. Or she may be depressed because she
cannot control her own depression from coming and going. The
“cure” in cases like these is not an array of choices but a choice.
Is maximizing always bad for people’s well-being? This seems
highly unlikely. Although relying on a maximizing strategy might
produce adverse consequences in some contexts, it is conceivable
that in others, maximizing will be an adaptive strategy. For example, an individual who responds to a health threat by searching for
information, asking questions, and striving to attain the best treatment available may get better results than someone who simply
selects a treatment that is sufficient. Maximizers may engage in
more active coping strategies such as planful problem solving and
seeking social support, whereas satisficers may cope by accepting
the situation and engaging in positive reappraisal. Some of these
coping responses may be more adaptive, others may be less
adaptive.
Thus, it seems that there are real advantages to adopting a
maximizing strategy. Presumably, not being satisfied with “good
enough” spurs one on to achievements that less ambitious people
will not attain, though there is as yet no evidence on this point.
Perhaps in the domain of action, greater achievements by maximizers compensate for lower satisfaction with those achievements,
whatever they are. But in the domain of consumption, the point of
which, after all, is subjective satisfaction, this compensatory feature of maximization is much less clear.
Caveats and Questions
Throughout this discussion, we have been treating maximizers
and satisficers as if there is a clear and distinct line that separates
them, measurable by some instrument such as our Maximization
Scale. But it is surely more accurate to say that people differ in the
extent to which they are maximizers, rather than falling on one or
the other side of a maximization line. That said, interesting research questions abound. There must be some variation from one
domain of choice to another in the extent to which one maximizes.
No one maximizes in all domains. For example, we doubt that
anyone searches for the prettiest postage stamp to affix to a federal
tax return. We have presented no data on the possible domain
specificity of a maximization orientation. Indeed, several of the
items on the Maximization Scale were written quite deliberately to
be vague as to domain. It is possible that where on the maximizing/
satisficing continuum one falls will be a reflection not of how high
one’s standards of acceptability are in general, but of how many
different domains of choice are dominated by a maximizing orientation. Research into the possible domain specificity of the
maximization orientation, and into whether maximizers and satisficers differ in the standards they apply to decisions in general or
in the number of domains in which they apply maximizing standards is in order. And beyond the matter of standards, it is
important to note whether maximizers and satisficers differ in their
scaling of the objective results of their decisions. It would be
interesting to know whether maximizers and satisficers respond
differently to measures of “objective happiness” recently pioneered by Kahneman (1999).
A second issue to be investigated is whether social comparison
is not the only kind of comparison to which maximizers are more
sensitive than satisficers. Michalos (1980, 1986), in his “multiple
discrepancies theory,” suggests that in assessing well-being, people evaluate not only what they have in relation to others, but also
what they have in relation to what they expected to have, what they
have had in the past, what they expect to have in the future, what
they need, and what they deserve. Each of these assessments is a
possible source of systematic differences between satisficers and
maximizers, with maximizers consistently experiencing larger
“gaps” between hopes and expectations on the one hand, and
reality on the other, than satisficers do.
Another possibility worth exploring is whether maximizers and
satisficers differ in what they perceive to be at stake when they
make decisions. For the satisficer, all that may be at stake is the
actual result of the decision—the quality of the good or the
experience that is chosen. For the maximizer, the results of choices
may, in addition, convey information about the self. That is,
maximizers may take the outcomes of decisions as evidence about
how smart, shrewd, or discerning they are as choosers. Each choice
a maximizer makes may say something important about the maximizer as a person. If it is true that maximizers have so much riding
on the outcomes of decisions, and if it is true, on the basis of
arguments made above, that the outcomes of decisions will usually
be disappointing to maximizers, it becomes unsurprising that maximizers are more depressed, more unhappy, and less optimistic
than satisficers. Related to this possibility is another. If maximizers
care more than satisficers about choices and their outcomes in
general, they may be vulnerable not only to choices presented by
the world, but to choices that they conjure up themselves. For
example, the maximizing university student might imagine that
there must be some way to combine a double major in finance and
biology (to keep both Master of Business Administration and
medical school futures open) with a minor in art, while spending
a semester in Thailand and a semester in Mexico, even though all
university rules indicate that this is impossible. The satisficer is
less likely to be plagued by opportunities that exist only in one’s
imagination.
Further, future research should examine process differences
between maximizers and satisficers when it comes to actual choice
behavior. That is, when actually making a choice, do maximizers
examine more options before selecting? Do they seek more information about alternatives? Do they desire more opportunities to
reverse decisions? Do they engage in more postdecision counterfactual thinking and experience more postdecision regret? Are they
1195
more adversely affected by multiple as opposed to few choice
options? The present article presents what we think are powerful
data on the relation between maximization and subjective experience. It remains to be determined whether maximizers also consistently act differently than satisficers.
Finally, we should note that in discussing the relation between
maximizing and dispositional happiness, we have been assuming
throughout the article that the causal arrow runs from maximizing
to unhappiness. Although this direction seems plausible to us, we
must acknowledge that alternative conceptualizations are possible.
For example, people who are dispositionally unhappy are likely to
be disappointed with the outcomes of many of their choices and
decisions. This disappointment may be (mis)attributed to the decisions themselves, rather than their own fundamental unhappiness, leading such individuals continually to strive to make “better” choices and judgments, in an ultimately fruitless effort to
enhance their happiness. Of course, such a process could also
produce a cyclical relationship, whereby unhappy individuals attempt to maximize (in a misguided effort to raise their affect),
leading to more unhappiness. It is critical for future research to
clarify whether being maximizers makes people unhappy or being
unhappy makes people maximizers. At this point, however, we
simply acknowledge that just as happiness may be a matter of
choice (i.e., how—and even whether—we make choices influences
whether we are happy or not), choice may also be a matter of
happiness.
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Received May 23, 2001
Revision received May 1, 2002
Accepted May 2, 2002 䡲
Journal of Happiness Studies (2005) 6:25–41
DOI 10.1007/s10902-004-1278-z
Springer 2005
CHRISTOPHER PETERSON, NANSOOK PARK and MARTIN
E.P. SELIGMAN
ORIENTATIONS TO HAPPINESS AND LIFE
SATISFACTION: THE FULL LIFE VERSUS THE
EMPTY LIFE
ABSTRACT. Different orientations to happiness and their association with
life satisfaction were investigated with 845 adults responding to Internet surveys. We measured life satisfaction and the endorsement of three different ways
to be happy: through pleasure, through engagement, and through meaning.
Each of these three orientations individually predicted life satisfaction. People
simultaneously low on all three orientations reported especially low life satisfaction. These findings point the way toward a distinction between the full life
and the empty life.
KEY WORDS: empty life, eudemonia, flow, full life, hedonism, life satisfaction, meaning
INTRODUCTION
Philosophers and psychologists have long been concerned with
the good life and how it can be achieved (Guignon, 1999; Russell,
1930, 1945). Often they propose a sovereign principle to be followed in order to be happy. So, the doctrine of hedonism –
maximizing pleasure and minimizing pain – was articulated
thousands of years ago by Aristuppus (435–366 BCE) who
championed immediate sensory gratification (Watson, 1895).
Hedonism was elaborated by Epicurus (342–270 BCE) into the
edict of ethical hedonism, which holds that our fundamental
moral obligation is to maximize our experience of pleasure. Early
Christian philosophers denounced hedonism as inconsistent with
the goal of avoiding sin, but Renaissance philosophers such as
Erasmus (1466–1536) and Thomas Moore (1478–1535) argued
that it was God’s wish that people be happy, so long as they
did not become preoccupied with ‘‘artificial’’ ways of achieving pleasure. Later British philosophers like David Hume
26
CHRISTOPHER PETERSON ET AL.
(1711–1776) and Jeremy Bentham (1748–1832) used the doctrine
of hedonism to lay the foundation for utilitarianism, which was
ushered into psychology as the underpinning of psychoanalysis
and all but the most radical of the behaviorisms. Hedonism is
alive and well today in the name of a new field – hedonic psychology (Kahneman et al., 1999). At least in the modern Western
world, the pursuit of pleasure is widely endorsed as a way to
achieve satisfaction: ‘‘Don’t worry – be happy.’’
Standing in contrast to hedonism is another venerable tradition
that can be traced to Aristotle’s (384–322 BCE) notion of eudemonia – being true to one’s inner self (demon). According to this
view, true happiness entails identifying one’s virtues, cultivating
them, and living in accordance with them (Aristotle, 2000).
Aristotle considered sensual pleasure as touted by the hedonists to
be vulgar. Similar positions were advanced by John Stuart Mill
(1806–1873) and Bertrand Russell (1872–1970) and undergird
more modern psychological notions such as Rogers’ (1951) ideal
of the fully-functioning person, Maslow’s (1970) concept of selfactualization, Ryff and Singer’s (1996) vision of psychological
well-being, and Deci and Ryan’s (2000) self-determination theory.
Uniting eudemonic emphases is the premise that people should
develop what is best within themselves and then use these skills
and talents in the service of greater goods – including in particular
the welfare of other people or humankind writ large. Again, in the
modern world, the pursuit of a meaningful life is widely endorsed
as a way to achieve satisfaction: ‘‘Be all that you can be,’’ and
‘‘Make a difference.’’
As implied, different psychological traditions have respectively addressed these two principles of achieving satisfaction.
Often these traditions have proceeded independently from
one another, with confusion introduced by the tendency of
those working within each tradition to claim ‘‘happiness’’ as a
label for their subject matter and to deny – if only implicitly – its
use by those in the other camp1. Sometimes the debate becomes
explicit, and we see investigators playing off the merits of pleasure
and meaning as routes to the psychological good life (e.g.,
Compton et al., 1996; Waterman, 1993). Research suggests that
both points of view can be supported by data (Ryan and Deci,
2000).
FULL LIFE VERSUS EMPTY LIFE
27
We extend this line of work by simultaneously examining the
pursuit of pleasure and the pursuit of meaning as different routes
to happiness. The unique contribution of our research is to consider a third orientation to happiness: the pursuit of engagement
(Seligman, 2002). Here we have been influenced by
Csikszentmihalyi’s (1990) writings on flow: the psychological state
that accompanies highly engaging activities. Time passes quickly.
Attention is focused on the activity. The sense of self is lost. The
aftermath of the flow experience is invigorating.
In his studies of eudemonia, Waterman (1993) initially equated
the flow state with eudemonia (which he termed personal
expressiveness) but then concluded from his data that flow represented an ‘‘amalgam’’ of hedonic and eudemonic features. We
suggest instead that flow is distinct. Flow is not the same as
sensual pleasure. Indeed, flow is nonemotional and arguably
nonconscious. People may describe flow as enjoyable, but this is
an after-the fact summary judgment; ‘‘joy’’ is not immediately
present during the activity itself. So, flow differs from hedonism,
in which positive emotional experience is front-and-center
(Csikszentmihalyi, 1999). At least at any given point in time, flow
and pleasure may even be incompatible.
Although the pursuit of a meaningful life can at times produce
flow for some individuals – e.g., those volunteering in a hospice or
a soup kitchen – not all flow-producing activities are meaningful
in the sense of connecting an individual to a greater good (consider playing bridge or Scrabble), and not all meaningful activities
entail the total absorption that defines flow. For example, each of
us does committee work at our university. At its best, this work is
meaningful, but we have yet to lose ourselves in its performance.
We report here an empirical investigation of these three ways of
being happy. The following questions guided our inquiry:
• Are these three orientations to happiness empirically distinguishable individual differences;
• Is an orientation to pleasure incompatible with an orientation
to engagement, as implied by the characterization of the
flow state as noncognitive and nonemotional, or is it possible
for the same person to pursue these different ways of being
happy;
28
• Do these three orientations each contribute to life satisfaction,
or are some more important than others; and
• Are there interactions between or among these orientations
with respect to life satisfaction; that is, does their joint presence predict more life satisfaction than expected from the
individual components, and, conversely, does their joint absence predict less than expected life satisfaction?
METHOD
Participants
Research participants were two groups of adult volunteers who
completed measures on-line. During initial instrument development, 180 respondents participated, and for the main study, 845
respondents participated. Demographic characteristics of these
two samples are summarized in Table I.
Measures
Orientations to Happiness
To develop the Orientations to Happiness measure used in this
study, 12 face-valid items reflecting each of the three orientations
were initially drafted by the authors and refined in a focus group
of college students (n ¼ 15) enrolled in a positive psychology class
at the University of Pennsylvania. Each item required a respondent to answer on a 5-point scale the degree to which the item
applied (‘‘1 ¼ very much unlike me’’ through ‘‘5 ¼ very much like
me’’). Items tapping pleasure and meaning resemble those used in
previous research contrasting hedonic versus eudemonic orientations (cf. King and Napa, 1998; McGregor and Little, 1998).
Items measuring engagement were based on Csikszentmihalyi’s
(1990) characterization of the flow state as self-less absorption in
ongoing activity.
Along with demographic questions, the initial 36-item Orientations to Happiness measure was placed on the Internet and
completed by participants in the instrument development sample
(n ¼ 180). Internal consistencies of the three subscales formed by
averaging the respective items, were satisfactory (pleasure
a ¼ 0.84, flow a ¼ 0.77, and meaning a ¼ 0.88) and exceeded the
29
TABLE I
Demographic characteristics of samples
Instrument
development
sample (n = 180) (%)
Main sample
(n = 845) (%)
Age (years)
18–20
21–24
25–34
35–44
45–54
55–64
65+
27
15
18
17
16
6
1
15
10
19
21
24
8
3
Gender
Male
Female
38
62
28
72
Education
<High school
High school graduate
Some college
Associates degree
Baccalaureate
>Baccalaureate
1
5
44
5
17
28
1
9
31
6
24
29
Marital status
Married/living as
Single
Widowed
Divorced
31
58
1
10
45
39
1
15
Ethnicity
African American
Asian American
Latino/a
White
Other
4
3
3
82
8
3
3
2
86
6
U.S. citizen
76
85
Town of residence
Farm
Country
Suburban
Small city (<50 K)
Medium city (<100 K)
1
6
19
16
15
1
12
26
17
15
30
Table I. (Continued)
Large city (<500 K)
Very large city (>500 K)
Political leaning
(1 = liberal,
7 = conservative)
Instrument
development
sample (n = 180) (%)
Main sample
(n = 845) (%)
16
27
16
13
M = 3.55
(SD = 1.85)
M = 3.37
(SD = 1.84)
subscale intercorrelations, which nonetheless were of moderate
magnitude (mean r ¼ 0.51). These results suggested that the three
orientations to happiness are distinguishable but related.
To sharpen the distinctions among the subscales, the six items
in each subscale with the highest item-total correlations were
chosen, and a revised 18-item measure was created and placed
online along with demographic questions to be completed by
participants in the main study (n ¼ 845). The revised version of
this scale is presented in Table II. Subscale means were calculated
by averaging the relevant items.
Satisfaction With Life Scale (SWLS) (Diener et al., 1985)
The SWLS consists of five items which measure the individual’s
evaluation of satisfaction with life in general (e.g., ‘‘I am satisfied
with my life,’’ and ‘‘If I could live my life over, I would change
almost nothing’’). Respondents select one of seven options
(ranging from ‘‘strongly disagree’’ to ‘‘strongly agree’’) for each
question. Responses were averaged to provide a total life satisfaction score. Research has established acceptable psychometric
properties for the SWLS (Diener, 1994). In the current study, the
SWLS was skewed toward the right, meaning that most respondents were relatively happy (cf. Diener and Diener, 1996; Myers
and Diener, 1995).
Procedure
All measures were placed online at www.positivepsychology.org/
strengths along with demographic questions (age; gender;
31
TABLE II
Orientations to happiness subscale items and factor loadings (n = 845)
Eigenvalue
% of variance
Life of meaning
02. My life serves a
higher purpose.
05. In choosing what to do,
I always take into account
whether it will benefit
other people.
11. I have a responsibility
to make the world a better place.
12. My life has a lasting meaning.
14. What I do matters to society.
17. I have spent a lot of time
thinking about what life means
and how I fit into its big picture.
Life of pleasure
03. Life is too short to postpone
the pleasures it can provide.
08. I go out of my way
to feel euphoric.
13. In choosing what to do,
I always take into account
whether it will be pleasurable.
15. I agree with this statement:
‘‘Life is short – eat dessert first.’’
16. I love to do things
that excite my senses.
18. For me, the good life
is the pleasurable life.
Life of engagement
01. Regardless of what I
am doing, time passes
very quickly.
04. I seek out situations
that challenge my
skills and abilities.
06. Whether at work or
play, I am usually ‘‘in a zone’’
and not conscious of myself.
Factor 1
4.96
28
Factor 2
2.80
16
Factor 3
1.60
9
0.75
0.01
0.08
0.54
0.01
0.32
0.79
)0.01
0.05
0.82
0.73
0.57
0.01
0.01
0.11
0.17
0.11
0.01
0.22
0.68
0.12
0.12
0.60
0.36
0.00
0.75
0.01
)0.01
0.71
)0.01
0.16
0.74
0.01
)0.01
0.79
0.00
0.23
0.00
0.48
0.40
0.12
0.51
0.00
0.01
0.75
32
Table II. (Continued)
Eigenvalue
% of variance
07. I am always very
absorbed in what I do.
09. In choosing what to
do, I always take into account
whether I can lose myself in it.
10. I am rarely distracted by
what is going on around me.
Factor 1
4.96
28
Factor 2
2.80
16
Factor 3
1.60
9
0.13
0.01
0.78
0.16
0.41
0.49
0.12
0.00
0.61
Note: Numbers in front of items correspond to order in the final Orientations
to Happiness measure. Entries in bold represent the factor on which item
loaded most highly.
education; marital status; ethnicity; U.S. citizenship; size of one’s
town of residence, from 1 ¼ farm through 7 ¼ very large city; and
liberal-versus-conservative political leaning assessed by a single 7point scale, from 1 ¼ liberal to 7 ¼ conservative). On the first
page of the web site, a short description of the study including its
purpose and its approximate time commitment was provided.
This web site also includes other psychological measures and
provides individualized feedback about an individual’s top scores
on these other measures upon completion of all measures (feedback on the Orientations to Happiness measure was not provided). The feedback feature is apparently attractive to potential
respondents and may explain why we did not need to advertise the
survey. To preserve respondent anonymity, we did not track how
individuals came across our survey on the Internet. They may
have learned about it by following a link on the Positive Psychology Webpage, by following links on other webpages, or by
hearing about it from previous respondents or from our media
interviews.
According to a recent study of Internet users by the UCLA
Center for Communication Policy (Lebo, 2003), more than 70%
of Americans use the Internet. Although certain limitations exist,
Internet surveys provide researchers with the opportunities to
recruit efficiently large and diverse samples at relatively little cost
(Birnbaum, 2000, 2004; Kraut et al., 2004; Schonlau et al., 2002).
33
RESULTS
For the main sample (n ¼ 845), a principal components analysis
of the 18 items in the revised Orientations to Happiness measure,
using varimax rotation, provided strong support for the a priori
assumption that it assessed three different orientations to the
good life. Three factors were identified with eigen-values greater
than 1.00, and each of the 18 items loaded most strongly on a
factor along with the other items intended to assess the same
orientation (Table II). Consistent with this analysis, internal
consistencies of the three subscales were satisfactory and exceeded
the scale intercorrelations (Table III). Subscale means, created by
averaging the appropriate items, could range from 1 to 5. Each
had a slight skew. Nevertheless, each subscale yielded a range of
scores.
There were no significant demographic variations in life satisfaction other than being married (Table III). However, several
TABLE III
Intercorrelations among measures (n = 845)
Pleasure Engagement Meaning Life
satisfaction
Pleasure
Engagement
Meaning
Life satisfaction
Age
Gender (1 = male, 2 = female)
Politics (1 = conservative,
7 = liberal)
Education
Married (0 = no, 1 = yes)
Home town size
White (0 = no, 1 = yes)
U.S. citizen (0 = no, 1 = yes)
0.31*
0.18*
0.17*
)0.22*
0.09
)0.02
0.46*
0.30*
0.07
)0.06
)0.08
0.26*
0.00
)0.03
)0.10
–
)0.09
0.06
)0.05
)0.19*
)0.17*
0.07
)0.07
)0.03
0.05
0.01
0.05
)0.11
)0.10
0.08
)0.03
0.05
)0.18*
)0.08
)0.01
0.17*
)0.05
)0.06
0.01
M
SD
a
Skew
3.20
0.84
0.82
)0.09
3.42
0.88
0.82
)0.32
4.93
1.37
0.90
)0.73
* p < 0.001.
–
–
3.05
0.72
0.72
0.08
–
34
modest demographic correlates of the Orientations to Happiness subscales were found. Those who were younger, less
educated, or unmarried were somewhat higher in their endorsement of an orientation to pleasure – as Aristotle might have
predicted. Ethnic minorities, chiefly African Americans and Asian
Americans, scored somewhat higher than Whites on orientation
to meaning.
Considered individually, each of the orientations to happiness
predicted life satisfaction, from small (pleasure) to moderate
(engagement, meaning) degrees (Table III). A hierarchical multiple regression predicting life satisfaction was then computed
TABLE IV
Hierarchical multiple regression predicting life satisfaction (n = 845)
b
Step 1
Age
Education
Gender (1 = male, 2 = female)
Politics (1 = conservative, 7 = liberal)
Home town size
U.S. citizen (0 = no, 1 = yes)
Married (0 = no, 1 = yes)
White (0 = no, 1 = yes)
Step 2
Pleasure
Engagement
Meaning
Step 3
Pleasure · engagement
Pleasure · meaning
Meaning · engagement
Step 4
Pleasure · engagement · meaning
)0.17*
0.01
0.08**
)0.05
)0.06
0.02
0.24*
)0.01
R2 = 0.066**
)0.11**
)0.24*
)0.17*
DR2 = 0.116*
)0.04
)0.03
0.00
DR2 = 0.004
)2.50**
DR2 = 0.006**
Note: Predictors in steps 1 and 2 were normalized (see text). b weights are for
the final model.
* p < 0.001; ** p < 0.05.
35
(Table IV), entering in the first step the demographic variables
(age, education, gender [1 ¼ male, 2 ¼ female], marital status
[married ¼ 1 or not ¼ 0], politics [1 ¼ conservative, 7 ¼ liberal],
US citizenship [yes ¼ 1 or no ¼ 1], size of home town [1 ¼ farm,
7 ¼ very large city], and ethnicity [White ¼ 1 versus not ¼ 0]), in
the second step by the subscales measuring the three orientations
to happiness, in the third step the two-way product terms
between the orientations (i.e., all possible pairs of three orientations), and in the last step the three-way product (pleasure ·
engagement · meaning).
Following the guidelines of Aiken and West (1991), we
centered the predictors in the first two blocks by normalizing
them and computed interactions by multiplying the relevant
normalized scores, but we did not center these product terms or
the criterion. Multicollinearity diagnostics were well within
acceptable limits. The overall regression was significant
(R2 ¼ 0.19, F [15,829] ¼13.15, p < 0.001). Above and beyond the
influence of demographic variables, the ‘‘main’’ effects of the three
orientations (all of which were significant predictors in the final
model), and the two-way interactions (none of which was statistically significant), the three-way interaction – entered in the last
step – was statistically significant, although small in its absolute
effect.
We therefore limited our attention to the three-way interaction.
To interpret it, we grouped the respondents in various ways on
each of the three subscales (e.g., low versus high, low versus
medium versus high, quartiles, quintiles, deciles, and so on) and
graphed life satisfaction scores as a joint function of these
groupings. No matter how we grouped the respondents, the same
patterns emerged: (a) somewhat higher life satisfaction scores for
respondents simultaneously near the top of all three Orientations
to Happiness subscales; and (b) notably lower life satisfaction
scores for respondents simultaneously near the bottom of all three
subscales. Figure 1 is a representative composite, where respondents simultaneously low on all three orientations (who have what
might be called the ‘‘Empty Life’’) reported the least life satisfaction, whereas those simultaneously high on all three orientations (who have what can be identified as the ‘‘Full Life’’)
reported the greatest life satisfaction.
36
Figure 1. Mean life satisfaction scores along the continuum of the empty life
versus the full life. Groups were created by identifying respondents simultaneously in the top 90% of each of the three subscales, simultaneously in the top
80% of each, and so on.
DISCUSSION
Drawing on past theory and research, we distinguished three
possible orientations to happiness (Seligman, 2002). The present
study found that these orientations are distinguishable, that
they are not incompatible and thus able to be pursued simultaneously, and that each is individually associated with life satisfaction. As previous research has shown, either hedonism or
eudemonia can accompany a satisfying life, and so too can
engagement.
Our interest was in people’s endorsement of these orientations
to happiness, and we did not ascertain whether those who said
they believe in pursuing pleasure actually have more sensually
gratifying experiences than those who do not, whether those who
supported engagement as an orientation to happiness more frequently lose themselves in highly absorbing activities, or whether
those who agreed with items reflecting a life of meaning are more
likely to perform service to others. We intend to pursue such
questions in further research, using a version of Waterman’s
37
(1993) procedure that asks respondents to report on ‘‘activities of
importance’’ and their features.
We also intend to study these orientations and their relationship to life satisfaction over time. We assume that given orientations shape conduct and thereby produce more or less
happiness, but the cross-sectional design of the present study does
not allow this notion to be tested. In particular, we need to
investigate the alternative interpretation of our data that a satisfying life leads to a diverse behavioral repertoire that includes
pleasurable, engaging, and meaningful activities (cf. Fredrickson,
2001). Although only trends in the present sample, we found it
interesting that the respondents with the fullest life were more
likely than those in other groups to be older, to be married, and to
be more highly educated – all factors that arguably open doors to
diverse and satisfying experiences.
The present research nonetheless extends theory in several ways,
suggesting in particular that an orientation to engagement differs
from orientations to pleasure or to meaning. Also, an orientation
to pleasure is not as strong an individual predictor of life satisfaction as orientations to engagement or to meaning. But neither is
pleasure irrelevant to life satisfaction, because it represents value
added to a life rich in engagement and meaning and value subtracted from a life deficient in these respects. The Full Life as we
have defined it predicts life satisfaction somewhat beyond the sum
of its parts, and the Empty Life predicts notably less.
Many psychologists who study pleasure seem unconvinced that
it can be increased, positing a genetically influenced set point of
affectivity to which most of us return following hedonically laden
experiences, either good or bad (Brickman and Campbell, 1971;
Kahneman, 1999). Perhaps the immutability of our ability to
experience pleasure explains why its pursuit can be futile
(Csikszentmihalyi, 1999). In contrast, a life of engagement
seems more under deliberate control (Massimini and Delle Fave,
2000), as does a life of meaning (Frankl, 1963). As positive psychology turns its attention to interventions that cultivate the good
life, perhaps interventions that target engagement and meaning
will prove most fruitful (Linley and Joseph, in press). However,
we would not want to rule out all attempts to increase pleasure,
and perhaps the way to boost pleasure is to follow one implication
38
of the present results that pleasure-enhancing techniques like
savoring be incorporated into those that increase engagement and
meaning (Linville and Fischer, 1991). Perhaps increased pleasure
can occur as a happy by-product of such interventions.
Besides our cross-sectional design, let us note an additional
limitation of our research, specifically our strategy of obtaining
research participants. Although increasingly common in psychological research, samples obtained from the World Wide Web are
often criticized because of the special characteristics of respondents. Individuals need to have access to a computer and the
ability to use it. They need to be interested enough to spend time
answering questions. But contrast our web sample with those
obtained from typical psychology subject pools and ask which
provides a sounder basis for generalization. Our respondents
ranged across the adult years and different levels of formal education. Men and women were represented. Many were married.
They lived in all parts of the United States or came from many
different countries. Our main sample included more than 100
(14%) non-White respondents. Respondents fell at all points
along the conservative-liberal political spectrum. Without belaboring the point, we observe that Internet samples are at least as
diverse as those recruited from psychology subject pool samples at
colleges or universities (Birnbaum, 2000, 2004; Kraut et al., 2004;
Lebo, 2003; Lenhart, 2000; Schonlau et al., 2002).
Many of the individuals who simultaneously scored low on all
three orientations were likely depressed, anxious, or otherwise
distressed. These are the people that clinical psychology has
studied for 50 years, grouping everyone else together as ‘‘normal’’
(Seligman and Csikszentmihalyi, 2000). We suggest that a distinction can be made along the continuum from the mere normal
to the supernormal. Investigating those people who have particularly high life satisfaction may well reveal ways of improving the
well-being of all of us (Diener and Seligman, 2002).
ACKNOWLEDGEMENTS
We acknowledge the support of the Manuel D. and Rhoda
Mayerson Foundation in creating the Values in Action Institute,
39
a nonprofit organization dedicated to the development of a scientific knowledge base of human strengths. Address correspondence to Christopher Peterson, Department of Psychology,
University of Michigan, 525 East University, Ann Arbor, MI
48109-1109; [email protected].
NOTES
1
In the present paper, we use ‘‘happiness’’ in its broadest sense to include
hedonic features but also fulfillment and contentment (cf. Myers, 1992; Seligman, 2002). We follow Diener’s (1984) lead in defining ‘‘life satisfaction’’ as a
summary appraisal of the quality of one’s life regardless of how it is achieved
(cf. Pavot and Diener, 1993).
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Psychology 74, pp. 494–512.
Myers, D.G.: 1992, The Pursuit of Happiness: Who is Happy – and Why
(William Morrow, New York).
Myers, D.G. and E. Diener: 1995, ‘Who is happy?’, Psychological Science 6,
pp. 10–19.
41
Pavot, W. and E. Diener: 1993, ‘Review of the satisfaction with life scale’,
Psychological Assessment 5, pp. 164–172.
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Russell, B.: 1930, The Conquest of Happiness (Liveright, New York).
Russell, B.: 1945, A History of Western Philosophy, and Its Connection With
Political and Social Circumstances from the Earliest Times to the Present
Day (Simon & Schuster, New York).
Ryan, R.M. and E.L. Deci: 2000, ‘On happiness and human potentials: A
review of research on hedonic and eudaimonic well-being’, Annual Review
of Psychology 52, pp. 141–166.
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Psychosomatics 65, pp. 14–23.
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Surveys via E-mail and the Web (RAND, Santa Monica, CA).
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introduction’, American Psychologist 55, pp. 5–14.
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Address for Correspondence:
CHRISTOPHER PETERSON
Department of Psychology
525 East University
Ann Arbor, MI 48109-1109
U.S.A
E-mail: [email protected]
Enhancing Well-Being and Alleviating Depressive
Symptoms With Positive Psychology Interventions:
A Practice-Friendly Meta-Analysis
m
Nancy L. Sin
m
Sonja Lyubomirsky
Do positive psychology interventions—that is, treatment methods or
intentional activities aimed at cultivating positive feelings, positive
behaviors, or positive cognitions—enhance well-being and ameliorate
depressive symptoms? A meta-analysis of 51 such interventions with
4,266 individuals was conducted to address this question and to
provide practical guidance to clinicians. The results revealed that
positive psychology interventions do indeed significantly enhance
well-being (mean r 5 .29) and decrease depressive symptoms (mean
r 5 .31). In addition, several factors were found to impact the
effectiveness of positive psychology interventions, including the
depression status, self-selection, and age of participants, as well as
the format and duration of the interventions. Accordingly, clinicians
should be encouraged to incorporate positive psychology techniques
into their clinical work, particularly for treating clients who are
depressed, relatively older, or highly motivated to improve. Our
findings also suggest that clinicians would do well to deliver positive
psychology interventions as individual (versus group) therapy and for
relatively longer periods of time. & 2009 Wiley Periodicals, Inc. J Clin
Psychol: In Session 65: 467–487, 2009.
Keywords: depression; meta-analysis; positive psychology; psychotherapy; treatment; well-being
Achieving greater happiness is an important goal for many people around the world.
Although most individuals are, in fact, happy (Diener & Diener, 1996), fewer than
We are grateful to Robert Rosenthal and Robin DiMatteo for their statistical advice.
Correspondence concerning this article should be addressed to: Sonja Lyubomirsky or Nancy Sin,
Department of Psychology, University of California, Riverside, CA 92521; e-mail: sonja.lyubomirsky@
ucr.edu, [email protected]
JOURNAL OF CLINICAL PSYCHOLOGY: IN SESSION, Vol. 65(5), 467--487 (2009)
& 2009 Wiley Periodicals, Inc.
Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/jclp.20593
468
Journal of Clinical Psychology: In Session, May 2009
20% of adults in the United States report that they are flourishing (Keyes, 2002).
Indeed, many appear to be languishing—that is, feeling as if they are ‘‘stuck in a rut’’
or ‘‘wanting more’’—yet are not diagnosable with a mental disorder (Fredrickson,
2008). Because happiness has been shown to be both the cause and the consequence
of many desirable life outcomes (including career success, marriage, and health;
Lyubomirsky, King, & Diener, 2005; Pressman & Cohen, 2005), it is vital to
understand how languishing individuals can be lifted to a more optimal state of
functioning. This spurs the millennia-old question: How does one enhance well-being
and relieve suffering?
Over the past decade, research in the field of positive psychology has emerged to
provide evidence-based answers and to guide practitioners. Increasingly, psychological well-being (WB) is viewed as not only the absence of mental disorder but also
the presence of positive psychological resources, including components of hedonic or
subjective well-being (e.g., positive affect, life satisfaction, happiness; Diener, 1984),
as well as components of eudaimonic well-being (e.g., self-acceptance, positive
relations, autonomy, purpose in life; Ryan & Deci, 2001; Ryff, 1989). A promising
approach to increase well-being is through positive psychology interventions
(PPIs)—that is, treatment methods or intentional activities that aim to cultivate
positive feelings, behaviors, or cognitions. For instance, PPI strategies as diverse as
writing gratitude letters, practicing optimistic thinking, replaying positive experiences, and socializing have been shown to increase WB in nonclinical samples (e.g.,
Fordyce, 1977; Lyubomirsky, Dickerhoof, Boehm, & Sheldon, 2008; Ruini, Belaise,
Brombin, Caffo, & Fava, 2006). It is worth noting, however, that programs,
interventions, or treatments aimed at fixing, remedying, or healing something that is
pathological or deficient—as opposed to building strengths—do not fit the definition
of a PPI.
In the past several years, research on PPIs for treating depression has proliferated.
Although PPIs may be an option for treating a variety of mental disorders (e.g.,
anxiety disorders; Fava et al., 2005), they can be particularly useful for addressing a
paucity of positive affect, engagement, and life meaning that characterize depression
(Forbes & Dahl, 2005; Seligman, Rashid, & Parks, 2006). For example, depressed
individuals are likely to benefit from increases in positive emotions, which have been
shown to speed recovery from the physiological effects of negative emotions
(Fredrickson & Levenson, 1998; Tugade & Fredrickson, 2004), to improve broadminded coping skills (Fredrickson & Joiner, 2002), and to prevent relapses (Fava &
Ruini, 2003).
However, PPI studies have provided mixed results. Some studies have shown that
positive psychotherapy, delivered to depressed individuals either in a group setting
or individually, significantly boosts WB and decreases depression (Fava et al., 2005;
Seligman et al., 2006). Other studies, however, have not found PPIs to be beneficial
overall compared with no-treatment control or placebo groups (e.g., Della Porta,
Sin, & Lyubomirsky, 2008; Froh, Sefick, & Emmons, 2008).
The general effectiveness of PPIs remains unclear, as no systematic quantitative
review of the research has yet been published. Solid empirical evidence is needed to
advance beyond anecdotal or clinical observations. Thus, the purpose of our metaanalysis was to provide guidance to clinical practitioners by answering the following
vital questions:
*
Do PPIs effectively enhance WB and ameliorate depression relative to control
groups and, if so, with what magnitude?
Journal of Clinical Psychology
DOI: 10.1002/jclp
Meta-Analysis of Positive Psychology Interventions
*
469
Which variables—with respect to both the characteristics of the participants and
the methodologies used—moderate the effectiveness of PPIs?
Because PPIs span multiple literatures, this meta-analysis is not comprehensive.
Instead, we primarily focus on PPIs conducted in positive psychology and include
samplings of PPIs from large, related literatures (e.g., mindfulness, life review
therapy, forgiveness therapy) to increase the generalizability of the findings.
Meta-Analysis Method
Literature Search
English-language PPI studies, conducted between 1977 and 2008, were located using
several search strategies. First, we searched our own libraries by hand for published
and unpublished studies. Next, we searched the PsycINFO online database using
combinations of the following keywords: depression, intervention, positive affect,
positive psychology, positive psychotherapy, well-being therapy, and well-being.
Furthermore, references for studies outside the positive psychology field were
gathered from colleagues. Finally, the reference sections of obtained papers, as well
as PsycINFO-generated listings of works that cite the papers, were examined for
additional PPI studies.
Studies were included in the meta-analysis if they met the following criteria:
1. The study must empirically test an intervention, therapy, or activity primarily
aimed at increasing positive feelings, positive behaviors, or positive cognitions, as
opposed to ameliorating pathology or fixing negative thoughts or maladaptive
behavior patterns.
2. Pre-intervention and post-intervention measures of WB or depression (as defined
by the Diagnostic and Statistical Manual of Mental Disorders-IV-TR; American
Psychiatric Association, 2000) must be included.
3. The study must have a comparison group, such as a no-treatment control, neutral
control, placebo, or treatment as usual.
4. The study must provide an effect size (or information to calculate an effect size)
for the strength and direction of the difference between the PPI group’s and
comparison group’s outcomes.
Interventions designed to improve physical well-being (health) or that involve
physical activity were excluded, as they are beyond the scope of this meta-analysis.
Furthermore, mood induction studies were excluded, as their aim is to boost
temporary mood, not psychological well-being.
Data Recorded
The specific measures of WB or depression, sample size, effect sizes, and one-tailed
Z’s associated with the significance levels were extracted from each study. In nearly
all cases, the r effect size for WB or depression was computed from Cohen’s d, F, t, p,
or descriptive statistics. If a result was reported as significant but did not provide an
exact probability, the one-tailed p value was assumed to be .025. If a result was
simply reported as nonsignificant and no data were provided to calculate an exact
probability, it was conservatively assigned p (one-tailed) 5 .5 and r 5 0. For studies
in which multiple measures of WB or depression were used, the r for each measure
was transformed to its corresponding Fisher Zr and then averaged to form a single
DOI: 10.1002/jclp
470
score (a conservative approach; Rosenthal & DiMatteo, 2001). Results in the
unpredicted direction (i.e., the comparison group experienced greater increases in
WB and greater decreases in depression compared with the PPI group) were
recorded as negative values of r.
In addition, the following information from each study was coded for the analysis
of moderators: depression status of participants (depressed or nondepressed);
participant age (child/adolescent up to 17 years old, young adult from 18 to 35 years
old, middle adult from 36 to 59 years old, or older adult of at least 60 years old);
whether participants self-selected to participate in the positive intervention (selfselected or non-self-selected); intervention format (individual therapy, groupadministered, or self-administered); intervention duration (up to 4 weeks, 5–7
weeks, 8–12 weeks, or more than 12 weeks); and comparison group type (notreatment control, neutral control, placebo, or treatment as usual). See Tables 1 and
2 for a complete list of studies and their relevant characteristics and results.
Statistical Analyses
Meta-analytic tests were conducted using both fixed effects and random effects
models. The fixed effects model, although limited in generalizability to the sample of
studies contained in the meta-analysis, is statistically powerful and appropriate for
small-sample meta-analyses (Rosenthal, 1995). The random effects model is less
powerful; however, it permits generalization to studies outside this sample
(Rosenthal, 1995).
Significance tests. Significance testing was conducted to determine the
probability that the sets of effect sizes for WB and depression are not significantly
different from zero. For the fixed effects model, one-tailed p values from each study
were converted to Z scores and then combined using the Stouffer method (e.g.,
Mosteller & Bush, 1954; Rosenthal & Rosnow, 2008, p. 673). For the random effects
model, one-sample t-tests were conducted on the mean Fisher Zr effect sizes
(Rosenthal, 1995).
Fail-safe N. To address the ‘‘file drawer problem’’—that is, the bias for
significant results to be more likely published and retrievable for a meta-analysis
relative to nonsignificant results—the fail-safe N was computed. This N represents
the number of studies averaging null results that must exist to render the overall
findings nonsignificant (Rosenthal, 1979, 1991a). The tolerance level was also
computed to estimate the number of irretrievable studies that possibly exist, based
on the assumption that the number of unpublished studies is not five times greater
than the number of published ones (Rosenthal & Rosnow, 2008, p. 689).
Heterogeneity test. A chi-square (w2) test was performed to determine the
probability that the obtained effect sizes are not heterogeneous (Rosenthal, 1991b,
p. 73). A highly significant chi-square result would suggest that moderator variables
may account for the heterogeneity of the effect sizes (Rosenthal & DiMatteo, 2001).
Moderator analyses. Predictions regarding moderators were translated into
linear contrast weights (l), and contrast tests were conducted on the effect sizes
based on the fixed effects model (Rosenthal, 1991b, p. 80; Rosenthal & DiMatteo,
2001). For the random effects model, the relationships between moderators and
effect sizes were analyzed by correlating Fisher Zr effect sizes with their
corresponding contrast weights (Rosenthal, 1991a).
DOI: 10.1002/jclp
SWLS; SHS; PA
Positive
reminiscence
Life review therapy
Gratitude
Gratitude
Gratitude
n
0.75
0.38
0.37
0.55
0.58
r
DOI: 10.1002/jclp
nondepressed
nondepressed
nondepressed
0.20
0.44
20
16
202 0.27
68
0.40
—
0.32
65
depressed
nondepressed
0.31 depressed
nondepressed
—
nondepressed
depressed
nondepressed
depressed
Depression
status
132 0.11
32
130 0.04
14
36
32
Mental health component of 13
the Short Form Health
Survey
PA
90
WB measure
State Hope Scale; Purpose in
Life Test
Life Satisfaction Index Form A
Life Satisfaction Index Form Z
SWLS; SHS; PA
Hope therapy
Positive writing
Mindfulness
Intervention
b
global life appraisals;
PA
Gratitude
global life appraisals; PA;
connectedness; observer
reports
Fava et al. (1998) WBT
Ryff’s PWB; 4 scales of WB
in SQ
Fava et al. (2005) CBT/WBT
in SQ
Fordyce (1977,
Fordyce’s happiness Happiness measures (selfStudy 1)
program
report scores for quality and
quantity of happiness)
Fordyce (1977,
Fordyce’s happiness Happiness measures
Study 2)
program
Della Porta et al.
(2008)
Della Porta et al.
(2008)
Emmons et al.
(2003, Study 1)
Emmons et al.
(2003, Study 3)
Davis (2004)
Burton et al.
(2004)
Cheavens et al.
(2006)
Cook (1998)
Bédard et al.
(2003)
Study
a
Studies Examining the Effect of PPI on Well-Being
Table 1
group-admin.
non-self-selected self-admin.
individual
therapy
self-selected
self-selected
self-selected
YA
YA
5–7 weeks
r4 weeks
individual therapy 412 weeks
placebo
placebo
TAU
TAU
no-treat ctrl
placebo
r4 weeks
r4 weeks
placebo
r4 weeks
neutral ctrl
—
r4 weeks
8–12 weeks
no-treat ctrl
no-treat ctrl
neutral ctrl
—
412 weeks
8–12 weeks
r4 weeks
8–12 weeks
Intervention Comparison
duration
group typed
MA self-selected
YA
MA non-self-selected self-admin.
YA
YA
YA
OA
OA
group-admin.
MA self-selected
YA
group-admin.
Intervention
delivery
Moderators
Self-selection
MA self-selected
Age
c
471
Cultivating sacred
moments
Life coaching/goals
Gratitude
Fordyce’s happiness
program
program
program
Forgiveness
Interventiona
12
Hope scale
PA; SWLS; Ryff’s PWB;
Hope Scale
PA
global life appraisals; PA;
BMSLSS
SWLS; Ryff’s PWB
0.31
57
DOI: 10.1002/jclp
nondepressed
0.56
0.63
0.09
33
48
35
—
nondepressed
nondepressed
119 0.27
depressed
nondepressed
nondepressed
nondepressed
nondepressed
0.32
0.33
0.00
nondepressed
depressed
nondepressed
nondepressed
nondepressed
Depression
status
0.19
0.13
55
35
52
50
73
141 0.02
0.84
0.32
0.45
r
98
68
n
Happiness measures; SelfDescription Inventory
Happiness measures
Happiness measures
WB measureb
PA
PA; SWLS; Life Orientation
Test
Kremers et al.
Self-Management
Social Production Function
(2006)
Index Level Scale
Lichter et al.
Discussing beliefs
pro-happy beliefs;
(1980, Study 1)
Affectometer 1; domain
satisfaction scores
Lichter et al.
Rehearsal of positive Affectometer 1; domain
(1980, Study 2)
statements
satisfaction scores
Low et al. (2006) Positive writing
PA (Vigor subscale of
POMS)
Grossman et al.
Mindfulness
(2007)
King et al. (2000) Positive writing
King (2001)
Positive writing
Green (2006)
Goldstein (2007)
Fordyce (1977,
Study 3)
Fordyce (1983,
Study 4)
Fordyce (1983,
Study 6)
Freedman et al.
(1996)
Froh et al. (2008)
Study
Table 1
Continued
self-admin.
Intervention
delivery
—
YA
—
OA
YA
YA
group-admin.
group-admin.
self-admin.
group-admin.
self-selected
self-admin.
non-self-selected group-admin.
self-selected
MA self-selected
MA self-selected
self-selected
C/A non-self-selected group-admin.
YA
5–7 weeks
8–12 weeks
5–7 weeks
no-treat ctrl
no-treat ctrl
no-treat ctrl
—
r4 weeks
r4 weeks
r4 weeks
neutral ctrl
neutral ctrl
r4 weeks
r4 weeks
5–7 weeks
TAU
8–12 weeks
no-treat ctrl
TAU
r4 weeks
8–12 weeks
no-treat ctrl
r4 weeks
no-treat ctrl
placebo
placebo
no-treat ctrl
duration
group typed
self-selected
Self-selection
MA self-selected
YA
YA
YA
Agec
Moderators
472
Goals
Goals
Optimism or
gratitude
Gratitude
Kindness
Interventiona
PA; SWLS
SHS; global happiness item;
delighted-terrible scale;
SWLS; PA
SHS; global happiness item;
delighted-terrible scale;
SWLS; PA
SHS; SWB (pleasant affect;
unpleasant affect
reversed; SWLS)
PA; SWLS
WB measureb
Counting kindnesses SHS
PPT
Students LS Scale; PPTI
Child Version
Forgiveness
Ryff’s PWB (Environmental
Mastery)
Ruini et al. (2006) WBT
in SQ
Seligman (2004)
PPT
SWLS
Seligman et al.
Gratitude/personal Steen Happiness Index
(2005)
strength activities
PPT
SWLS
Seligman et al.
(2006, Study 1)
Seligman et al.
PPT
PPTI; SWLS
(2006, Study 2)
MacLeod et al.
(2008, Study
1)
MacLeod et al.
(2008, Study
2)
Otake et al. (2006)
Rashid et al.
(2006)
Reed et al. (2006)
Lyubomirsky et
al. (2004, Study
1)
Lyubomirsky et
al. (2004, Study
2)
Lyubomirsky et
al. (2008)
Study
Continued
Table 1
0.00
r
depressed
DOI: 10.1002/jclp
depressed
depressed
depressed
185 0.16
411 0.06
0.20
0.40
34
32
depressed
nondepressed
111 0.06
0.52
20
nondepressed
nondepressed
nondepressed
nondepressed
0.52
0.24
nondepressed
nondepressed
nondepressed
Depression
status
119 0.20
22 0.32
20
64
315 0.16
147 0.00
84
n
Intervention
delivery
self-selected
self-selected
self-selected
self-selected
YA
self-selected
MA self-selected
YA
—
r4 weeks
8–12 weeks
5–7 weeks
412 weeks
r4 weeks
individual therapy 8–12 weeks
group-admin.
self-admin.
self-admin.
8–12 weeks
TAU
no-treat ctrl
no-treat ctrl
placebo
TAU
TAU
no-treat ctrl
no-treat ctrl
no-treat ctrl
no-treat ctrl
r4 weeks
5–7 weeks
—
no-treat ctrl
no-treat ctrl
8–12 weeks
5–7 weeks
5–7 weeks
duration
group typed
C/A non-self-selected self-admin.
MA self-selected
YA non-self-selected self-admin.
C/A non-self-selected self-admin.
self-admin.
group-admin.
self-admin.
Self-selection
MA self-selected
YA
YA
YA
YA
Agec
Moderators
473
Goal training
Interventiona
DOI: 10.1002/jclp
SWLS
PA
PA
Positive writing
Mindfulness
Mindfulness
Gratitude
PA; SWLS; SHS; Ryff’s
PWB
PA
Happiness Inventory;
Psychap Measure
PA; SWB; Ryff’s PWB
PA; Ryff’s PWB; selfactualization
questionnaire
PA
WB measureb
0.01
57
nondepressed
nondepressed
nondepressed
nondepressed
Depression
status
20
0.22
175 0.00
71 0.15
156 0.16
depressed
nondepressed
nondepressed
nondepressed
285 0.04 nondepressed
0.80
0.28
0.00
r
36
67
78
n
group-admin.
self-admin.
group-admin.
MA self-selected
5–7 weeks
r4 weeks
r4 weeks
neutral ctrl
placebo
r4 weeks
8–12 weeks
placebo
neutral ctrl
r4 weeks
8–12 weeks
neutral ctrl
8–12 weeks
no-treat ctrl
no-treat ctrl
placebo
neutral ctrl
duration
group typed
individual therapy 8–12 weeks
MA self-selected
MA self-selected
YA
YA
Intervention
delivery
Self-selection
MA self-selected
YA
YA
YA
Agec
Moderators
Note. Dash (—) indicates information could not be found, and the study was excluded from the respective moderator analysis. Positive effect sizes indicate superiority of PPI
over comparison group for increasing WB. Negative effect sizes indicate superiority of comparison group over PPI for increasing WB.
a
CBT 5 cognitive behavioral therapy; PPT 5 positive psychotherapy; WBT 5 well-being therapy.
b
BMSLSS 5 Brief Multidimensional Students’ Life Satisfaction Scale; LS 5 life satisfaction; PA 5 positive affect; PPTI 5 Positive Psychotherapy Inventory; PWB 5 Psychological Well-Being Scale; SHS 5 Subjective Happiness Scale; SWB 5 Subjective Well-Being; SWLS 5 Satisfaction With Life Scale; WB 5 well-being.
c
C/A 5 child/adolescent (up to 17 years old); YA 5 young adult (18 to 35 years old); MA 5 middle adult (35 to 59 years old); OA 5 older adult (60 years old and up).
d
Ctrl 5 control; TAU 5 treatment as usual.
Watkins et al.
(2003, Study 4)
Wing et al. (2006)
Zautra et al.
(2008, Study 1)
Zautra et al.
(2008, Study 1)
Sheldon et al.
Gratitude
(2006)
Smith et al. (1995) Fordyce’s happiness
program/meditation
Spence et al.
Life coaching/goals
(2007)
Tkach (2006)
Kindness
Sheldon et al.
(2002)
Study
Table 1
Continued
474
DOI: 10.1002/jclp
CES-D
Children’s
Depression
Inventory
Forgiveness
Optimism or gratitude
PPT
Lin et al. (2004)
Lyubomirsky et al.
(2008)
Rashid et al. (2006)
BDI-II
BDI
Rehearsal of positive
statements
Forgiveness
HADS
program
Fordyce (1983,
Study 6)
Mindfulness
program
Fordyce (1983,
Study 4)
Freedman et al.
(1996)
Grossman et al.
(2007)
Lichter et al.
(1980, Study 2)
CBT/WBT
Fava et al. (2005)
CID; Kellner’s
SQ
CID; Kellner’s
SQ
Depression
Adjective
Check List
Depression
Adjective
Check List
BDI
BDI-II
Gratitude
WBT
BDI-II
Zung
BDI-II
CES-D
Depression
measureb
Gratitude
Life review therapy
Mindfulness
Hope therapy
Intervention
Fava et al. (1998)
Della Porta et al.
(2008)
Della Porta et al.
(2008)
Bédard et al. (2003)
Cheavens et al.
(2006)
Davis (2004)
Study
a
Studies Examining the Effect of PPI on Depression
Table 2
0.03
0.04
22
0.47
0.50
0.30
0.75
315
14
48
52
12
0.26
0.21
98
57
0.40
0.32
0.14
0.11
0.81
0.56
0.35
r
16
20
32
130
14
13
32
n
nondepressed
nondepressed
depressed
nondepressed
depressed
depressed
nondepressed
nondepressed
nondepressed
depressed
depressed
nondepressed
—
depressed
depressed
Depression status
C/A
YA
MA
YA
MA
MA
YA
YA
MA
YA
YA
YA
OA
MA
MA
Age
c
non-self-selected
self-selected
self-selected
non-self-selected
self-selected
self-selected
non-self-selected
non-self-selected
self-selected
self-selected
non-self-selected
non-self-selected
self-selected
self-selected
self-selected
Self-selection
self-admin.
individual
therapy
self-admin.
self-admin.
individual
therapy
group-admin.
self-admin.
individual
therapy
individual
therapy
self-admin.
self-admin.
individual
therapy
self-admin.
group-admin.
group-admin.
Intervention
delivery
Moderators
placebo
r4 weeks
8–12 weeks
8–12 weeks
no-treat ctrl
—
TAU
no-treat ctrl
r4 weeks
5–7 weeks
TAU
no-treat ctrl
placebo
placebo
TAU
8–12 weeks
412 weeks
5–7 weeks
8–12 weeks
412 weeks
TAU
placebo
r4 weeks
412 weeks
—
—
no-treat ctrl
Comparison
group typed
r4 weeks
8–12 weeks
8–12 weeks
Intervention
duration
475
DOI: 10.1002/jclp
Mindfulness
Mindfulness
BDI
program/meditation
Mindfulness
depressive
symptoms
questionnaire
depressive
symptoms
questionnaire
HADS
HRSD; Zung
PPT
0.23
0.01
71
20
0.28
0.73
17
36
0.51
0.25
0.15
0.15
0.10
0.53
r
depressed
nondepressed
depressed
nondepressed
depressed
depressed
nondepressed
depressed
depressed
depressed
Depression status
MA
MA
—
YA
YA
MA
C/A
YA
—
MA
Agec
self-selected
self-selected
self-selected
non-self-selected
self-selected
self-selected
non-self-selected
self-selected
self-selected
self-selected
Self-selection
group-admin.
group-admin.
group-admin.
individual
therapy
group-admin.
group-admin.
individual
therapy
self-admin.
self-admin.
self-admin.
Intervention
delivery
Moderators
8–12 weeks
8–12 weeks
8–12 weeks
5–7 weeks
8–12 weeks
5–7 weeks
8–12 weeks
412 weeks
r4 weeks
412 weeks
Intervention
duration
placebo
placebo
no-treat ctrl
no-treat ctrl
TAU
no-treat ctrl
TAU
no-treat ctrl
placebo
TAU
Comparison
group typed
Note. Dash (—) indicates information could not be found, and the study was excluded from the respective moderator analysis. Positive effect sizes indicate superiority of PPI
over comparison group for decreasing depressive symptoms. Negative effect sizes indicate superiority of comparison group over PPI for decreasing depressive symptoms.
a
CBT 5 cognitive behavioral therapy; PPT 5 positive psychotherapy; WBT 5 well-being therapy.
b
BDI 5 Beck Depression Inventory; CES-D 5 Center for Epidemiology–Depression Scale; CID 5 Clinical Interview for Depression; HADS 5 Hospital Anxiety and Depression
Scale; HRSD 5 Hamilton Rating Scale for Depression; SQ 5 Symptom Questionnaire.
c
C/A 5 child/adolescent (up to 17 years old); YA 5 young adult (18 to 35 years old); MA 5 middle adult (35 to 59 years old); OA 5 older adult (60 years old and up).
d
Ctrl 5 control, TAU 5 treatment as usual.
Zautra et al. (2008,
Study 1)
Surawy et al. (2005,
Study 1)
Zautra et al.
(2008, Study 1)
34
BDI-II
32
111
185
411
20
n
Kellner’s SQ
CES-D
CES-D
Ruini et al. (2006)
Seligman (2004)
Seligman et al.
(2005)
Seligman et al.
(2006, Study 1)
Seligman et al.
(2006, Study 2)
Smith et al. (1995)
BDI-II
Depression
measureb
WBT
PPT
Gratitude/personal
strength activities
PPT
Forgiveness
Interventiona
Reed et al. (2006)
Study
Table 2
Continued
476
477
Meta-Analysis Results
Overall, PPIs were indeed significantly more effective than comparison groups for
boosting WB and for ameliorating depression. Table 3 shows a stem-and-leaf display
of all effect sizes, and Table 4 summarizes the meta-analytic findings. The relevant
statistics for all the moderator analyses are presented in Tables 5 and 6.
Well-Being
Forty-nine independent studies were meta-analyzed for WB, totaling 4,235
participants (median n 5 64 per study). The r effect sizes ranged from .31 to .84,
with 96% of effect sizes in the predicted, positive direction. The unweighted mean r
(.29) was close in magnitude to the median r (.24) and was highly significant based on
both the fixed effects (one-tailed p 5 0) and the random effects (one-tailed
p 5 4 109) models. Although the file drawer problem (indicated by the asymmetric
funnel plot of the stem-and-leaf display; Light & Pillemer, 1984), was likely present,
it was not large enough to render the overall results nonsignificant. Indeed, the failsafe N indicated that 2,519 studies averaging null results must exist to render this
finding nonsignificant; this number greatly exceeds the tolerance level of 255
unpublished null studies that possibly exist. Furthermore, the set of effect sizes was
heterogeneous (w2ð48Þ ¼ 230:92, one-tailed p 5 9 1026), suggesting the presence of
moderator variables.
Depression
The meta-analysis for depression encompassed 25 separate studies, with a median of
32 participants per study and a grand total of 1,812 participants. The r effect sizes
spanned from .28 to .81, with 80% of effect sizes in favor of PPI. The unweighted
mean r of .31 was close to the median r (.26) and was highly significant based on both
the fixed effects (one-tailed p 5 2 1012) and the random effects (one-tailed
p 5 .0001) models. The file drawer problem was unlikely to threaten the significant
results, given that the fail-safe N was 420, which exceeded the tolerance level of 135.
Moreover, as in the case of well-being, the set of effect sizes was heterogeneous
Table 3
Back-to-Back Stem-and-Leaf Display of all Effect Sizes
WELL-BEING (k 5 49)
8, 7, 3,
8, 7, 7,
9, 6,
9, 6, 6, 4, 2, 1, 0,
8, 6,
5,
2, 2,
4, 2,
6, 6,
0, 0,
DEPRESSION (k 5 25)
Leaf
4, 0
5
3
5, 2, 2
4, 0, 0
2, 2, 1
0, 0, 0
5, 3, 1
0, 0, 0
1
Stem
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
0.1
0.2
0.3
Leaf
1
3, 5
0,
0,
0,
1,
0,
0,
1,
8
1,
7
2,
3,
5
3,
4,
3, 6
5
5, 6
4
5
Note. ‘‘k’’ is the number of studies included in the meta-analysis.
DOI: 10.1002/jclp
49
25
4,235
1,812
64
32
.29 [.21, .37]
.31 [.17, .43]
0.24
0.26
Fixed
p
2,519 (255)
420 (135)
Random Fail-safe N (tolerance)
.31, .84 .24
0 4 109
.28, .81 .29 2 1012
.0001
No. of studies Total n Median n per study Mean [95% CI]b Median Min, max SD
Unweighted r effect size
Note. All p values are one-tailed.
a
Degrees of freedom is the number of studies minus 1.
b
Confidence intervals are computed based on the stringent random effects model.
Well-Being
Depression
Variable
Sample size
Summary of Overall Meta-Analysis Findings
Table 4
230.92
146.32
w2
9 1026
2 1019
P
Test of heterogeneitya
478
DOI: 10.1002/jclp
479
(w2ð24Þ ¼ 146:32, one-tailed p 5 2 1019), indicating that moderators may account
for the variation in effect sizes.
Participant Moderators
Depression status. Depressed individuals benefited more from PPIs than
nondepressed individuals, according to the fixed effects moderator analysis.
However, the random effects moderator analysis did not find a significant
moderating effect for depression status. The mean effect sizes, and other relevant
statistics for this and all other moderators, are displayed in Tables 5 and 6.
Self-selection. Individuals who elected to participate in a PPI—perhaps
expecting that the intervention would make them happier or less depressed—were
more likely to experience greater gains in WB and depression compared with their
non-self-selected counterparts, based on the fixed effects model. However, the
moderating effect of self-selection was not significant for WB or depression
according to the random effects model.
Age. Both the fixed effects and random effects models revealed that the benefits
of PPIs increase with age. Although there were not enough studies to include the
‘‘older adult’’ group in the moderator analysis for depression, the other age groups
nevertheless showed larger decreases in depression as a linear function of age.
Methodological Moderators
Intervention format. Intervention format had a significant moderating effect on
the effectiveness of PPIs, based on both the fixed effects and the random effects
models. Specifically, the mean r effect size was greatest for individual therapy,
followed by group-administered PPIs, then self-administered PPIs.
Intervention duration. Longer interventions produced greater gains in WB based
on the fixed effects model, and this effect approached significance according to the
random effects model (one-tailed p 5 .09). For depression, however, the moderating
effect of intervention duration was not significant based on both the fixed effects and
random effects models.
Comparison group type. The fixed effects analysis revealed that the type of
comparison group used in a PPI moderated the gains in WB but not when computed
using the random effects analysis. In particular, studies that compared a PPI to a notreatment control group showed the greatest boost in WB. Positive interventions that
were compared with ‘‘treatment as usual’’ showed the second greatest benefit,
suggesting that PPIs may be more effective than standard treatments. Finally, PPIs
also produced greater boosts in WB compared with neutral control and placebo
activities. The type of comparison group did not have a moderating effect on shifts in
depression, based on both the fixed effects and the random effects models. This
indicates that participation in PPIs may be more effective for relieving depression
than participation in a no-treatment control group, placebo activity, or treatment as
usual.
Clinical Practices
Do positive psychology interventions effectively boost well-being and ameliorate
depression? The overwhelming evidence from our meta-analysis suggests that the
DOI: 10.1002/jclp
DOI: 10.1002/jclp
2023
633
1290
289
1372
1006
1156
324
20
9
14
6
20
8
10
7
274
2710
603
169
3
27
13
3
171
779
3285
1944
2291
25
24
7
14
28
863
3258
Total n
12
34
No. of studies
po.05 (one-tailed).po.01 (one-tailed). po .001 (one-tailed).
Methodological moderators
Intervention format
Individual therapy
Group-administered
Self-administered
Intervention duration
r 4 weeks
5–7 weeks
8–12 weeks
4 12 weeks
Comparison group type
No-treatment control
Neutral control
Placebo
Treatment as usual (TAU)
Participant moderators
Depression status
Depressed
Nondepressed
Self-selection
Self-selected
Non-self-selected
Age
Child/adolescent (r 17 years old)
Young adult (18–35 years old)
Middle adult (36–59 years old)
Older adult ( 60 years old)
Moderator
Summary of Moderator Findings for Well-Being
Table 5
0.37
0.14
0.18
0.29
0.23
0.36
0.24
0.47
0.50
0.34
0.20
0.14
0.23
0.39
0.50
0.33
0.24
0.33
0.26
Unweighted mean r
0.32
0.12
0.25
0.32
0.18
0.31
0.27
0.41
0.44
0.30
0.16
0.06
0.20
0.33
0.38
0.27
0.20
0.27
0.22
Unweighted median r
13
11
1
3
3
1
11
13
11
0
1
3
1
11
13
11
1
11
1
l weight
z 5 5.63
z 5 1.73
z 5 1.88
z 5 1.47
z 5 2.28
z 5 4.39
Fixed
r 5 .20
r 5 .20
r 5 .42
r 5 .35
r 5 .17
r 5 .12
Random
Moderator analysis
480
DOI: 10.1002/jclp
635
141
783
253
386
819
265
5
4
11
5
8
7
7
128
275
1409
133
987
250
2
11
9
7
8
10
1272
534
894
904
Total n
17
8
14
10
No. of studies
0.35
0.08
0.35
0.29
0.46
0.18
0.46
0.57
0.30
0.09
0.06
0.27
0.42
0.35
0.20
0.32
0.21
Unweighted mean r
0.30
0.10
0.40
0.10
0.37
0.21
0.40
0.56
0.28
0.07
0.06
0.25
0.40
0.32
0.13
0.31
0.13
Unweighted median r
b
‘‘Older adult’’ was excluded in this analysis because there was only 1 study (n 5 14) in that category.
‘‘Neutral control’’ was excluded from this analysis because no studies had a ‘‘neutral control’’ comparison group.
po.05 (one-tailed).po .01 (one-tailed).po.001 (one-tailed).
a
Methodological moderators
Intervention format
Individual therapy
Group-administered
Self-administered
Intervention duration
r4 weeks
5–7 weeks
8–12 weeks
4 12 weeks
Comparison group typeb
No-treatment control
Placebo
Treatment as usual (TAU)
Participant Moderators
Depression Status
Depressed
Nondepressed
Self-Selection
Self-selected
Non-self-selected
Agea
Child/adolescent (r 17 years old)
Young adult (18–35 years old)
Middle adult (36–59 years old)
Moderator
Summary of Moderator Findings for Depression
Table 6
11
0
1
3
1
11
13
11
0
1
1
0
11
11
1
11
1
l weight
z 5 .44
z 5 1.23
z 5 4.07
z 5 4.95
z 5 4.42
z 5 2.36
Fixed
r 5 .02
r 5 .04
r 5 .63
r 5 .45
r 5 .23
r 5 .17
Random
Moderator analysis
481
482
Table 7
Binomial Effect Size Displays of the Benefits of PPI
Lower well-being
Higher well-being
Total
Effect of PPI on well-being (r 5 .29)
PPI
Control
Total
35
65
100
65
35
100
100
100
200
Effect of PPI on depression (r 5 .31)
PPI
Control
Total
34
66
100
66
34
100
100
100
200
answer is ‘‘yes.’’ The combined results of 49 studies revealed that PPIs do, in fact,
significantly enhance WB, and the combined results of 25 studies showed that PPIs
are also effective for treating depressive symptoms. The magnitude of these effects is
medium-sized (mean r 5 .29 for WB, mean r 5 .31 for depression), indicating that
not only do PPIs work, they work well.
The practical importance of these effect sizes is illustrated using a binomial effect
size display, as shown in Table 7. In a hypothetical group of 200 individuals—half
the group treated with a PPI, and half the group untreated—65 individuals treated
with a PPI would experience gains in WB, compared with only 35 of the individuals
in the control group. Similarly, 66 PPI-treated individuals would experience
decreased depression, relative to only 34 individuals in the control group.
It is likely that—in addition to learning how to cope with negatives—clients will
greatly benefit from attending to, appreciating, and attaining life’s positives. Thus,
practitioners are encouraged to incorporate elements of positive psychology into
their clinical work. A variety of PPIs have now been found effective, including
positive behaviors like engaging in enjoyable activities (Fordyce, 1977) and using
one’s signature strengths in new ways (Seligman, Steen, Park, & Peterson, 2005).
Cognitive strategies, such as replaying positive experiences and self-monitoring
instances of well-being, have also been shown to boost happiness and alleviate
depression (Fava, Rafanelli, Cazzaro, Conti, & Grandi, 1998). Finally, the practice
of emotional skills—including mindfulness and acceptance—can have a positive
impact on a client’s psychological well-being (Bédard et al., 2003; Grossman,
Tiefenthaler-Gilmer, Raysz, & Kesper, 2007; Zautra et al., 2008).
Our moderator analyses also point to specific clinical practices. First, depression
status moderated the effectiveness of PPIs, such that depressed individuals
experienced enhanced WB and reduced depressive symptoms relative to nondepressed ones. This finding could be accounted for by a floor effect—that is,
depressed individuals may simply have more room to improve—or it may have a
substantive explanation. In any case, our result challenges the notion that depressed
people might benefit less from PPIs, because their characteristic cognitive, affective,
and behavioral deficits prevent them from taking full advantage of the relevant
positive activities. Accordingly, practitioners are advised to implement PPIs in the
treatment of both clinically depressed and nondepressed clients, as both are likely to
garner the benefits. PPIs can be especially effective for treating residual symptoms
(Fava et al., 1998) and preventing future relapse for formerly depressed clients
(Seligman et al., 2006).
DOI: 10.1002/jclp
483
Second, self-selected individuals—those who possibly were more motivated or
who expected the intervention to make them happier—benefited more from PPIs
than did their non–self-selected peers. We did not find this surprising, as those with
higher levels of motivation or more positive, optimistic expectancies would be
expected to work harder and longer at following the instructions of a PPI, to show
greater commitment, and to take their assigned task more seriously. Accordingly,
practitioners will do well to bolster motivation and provide ample encouragement to
clients, particularly to those who are initially reluctant to engage in treatment.
Third, the benefits of PPIs increased linearly with age, perhaps due to the greater
wisdom and more effective emotional regulation and self-regulation associated with
older age (Carstensen, Isaacowitz, & Charles, 1999; Linley et al., 2007). Older
participants may also have treated the PPI with greater seriousness and maturity and
applied more effort in carrying out its recommendations. Thus, practitioners may see
more improvement when offering PPIs to older, rather than younger, clients.
Younger individuals, as well as those who may be less emotionally invested in their
treatment, should be encouraged to devote more effort and commitment.
Fourth, the format of the treatment also moderated the effectiveness of PPIs:
Individual therapy was most effective, followed by group-administered PPIs, then
self-administered PPIs. Hence, practitioners offering PPIs as part of individual
therapy, and secondarily group therapy, can expect the highest benefits for their
clients. The duration of PPIs also moderated their benefits, such that longer
interventions were relatively more likely to produce greater gains in WB. Longer
durations give participants an opportunity to convert the positive activities they are
learning into habits.
Please bear in mind that these findings should be interpreted cautiously, as this
meta-analysis is not comprehensive (i.e., does not include all PPI studies ever
conducted). In addition, causal conclusions cannot be drawn regarding the
moderators because the studies were not randomly assigned to particular moderator
levels (Rosenthal, 1991b, p. 81).
Finally, we offer three suggestions for clinical practice based on the extant
research on PPIs, although these are not directly derived from our meta-analysis.
*
*
*
Several studies have found that exerting high levels of effort to practice a
happiness-boosting strategy, and continuing to practice it even after the
intervention is over, results in greater improvements in WB and depression
(e.g., Lyubomirsky et al., 2008; Seligman et al., 2005). Thus, clinicians should
encourage their clients to regularly practice and keep a record of positive
strategies, to incorporate these strategies into their everyday lives, and to turn
these strategies into habits.
It also appears that a ‘‘shotgun’’ approach, in which individuals practice multiple
PPI activities, may be more effective than engaging in only one activity (e.g.,
Fordyce 1977, 1983; Seligman et al., 2005). Accordingly, practitioners may see the
most benefit in their clients when assigning multiple and different positive
activities.
Members of individualist cultures, whose values and cultural prescriptives are
highly supportive of the pursuit of individual happiness, have been found to
benefit more from PPIs than members of collectivist cultures (Lyubomirsky et al.,
2008). As a result, clinicians are advised to consider a client’s cultural
background, as well as his or her unique inclinations, when implementing PPIs.
For instance, a client from a collectivist culture may experience greater boosts in
DOI: 10.1002/jclp
484
well-being when practicing prosocial and other-focused activities (e.g., performing
acts of kindness, writing a letter of gratitude), compared with individual-focused
activities (e.g., reflecting on personal strengths).
The field of positive psychology is young, yet much has already been accomplished
that practitioners can effectively integrate into their daily practices. As our metaanalysis confirms, positive psychology interventions can materially improve the wellbeing of many.
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