ArticlePDF Available

Abstract and Figures

We examined the life-span development of self-esteem and tested whether self-esteem influences the development of important life outcomes, including relationship satisfaction, job satisfaction, occupational status, salary, positive and negative affect, depression, and physical health. Data came from the Longitudinal Study of Generations. Analyses were based on 5 assessments across a 12-year period of a sample of 1,824 individuals ages 16 to 97 years. First, growth curve analyses indicated that self-esteem increases from adolescence to middle adulthood, reaches a peak at about age 50 years, and then decreases in old age. Second, cross-lagged regression analyses indicated that self-esteem is best modeled as a cause rather than a consequence of life outcomes. Third, growth curve analyses, with self-esteem as a time-varying covariate, suggested that self-esteem has medium-sized effects on life-span trajectories of affect and depression, small to medium-sized effects on trajectories of relationship and job satisfaction, a very small effect on the trajectory of health, and no effect on the trajectory of occupational status. These findings replicated across 4 generations of participants—children, parents, grandparents, and their great-grandparents. Together, the results suggest that self-esteem has a significant prospective impact on real-world life experiences and that high and low self-esteem are not mere epiphenomena of success and failure in important life domains.
Content may be subject to copyright.
PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES
Life-Span Development of Self-Esteem and Its Effects on
Important Life Outcomes
Ulrich Orth
University of Basel Richard W. Robins and Keith F. Widaman
University of California, Davis
We examined the life-span development of self-esteem and tested whether self-esteem influences the
development of important life outcomes, including relationship satisfaction, job satisfaction, occupational
status, salary, positive and negative affect, depression, and physical health. Data came from the
Longitudinal Study of Generations. Analyses were based on 5 assessments across a 12-year period of a
sample of 1,824 individuals ages 16 to 97 years. First, growth curve analyses indicated that self-esteem
increases from adolescence to middle adulthood, reaches a peak at about age 50 years, and then decreases
in old age. Second, cross-lagged regression analyses indicated that self-esteem is best modeled as a cause
rather than a consequence of life outcomes. Third, growth curve analyses, with self-esteem as a
time-varying covariate, suggested that self-esteem has medium-sized effects on life-span trajectories of
affect and depression, small to medium-sized effects on trajectories of relationship and job satisfaction,
a very small effect on the trajectory of health, and no effect on the trajectory of occupational status. These
findings replicated across 4 generations of participants—children, parents, grandparents, and their
great-grandparents. Together, the results suggest that self-esteem has a significant prospective impact on
real-world life experiences and that high and low self-esteem are not mere epiphenomena of success and
failure in important life domains.
Keywords: self-esteem, life-span development, life outcomes
There is an ongoing debate about whether individuals with high
self-esteem have better prospects for their life than individuals
with low self-esteem. Whereas some studies suggest that global
self-esteem—a person’s overall evaluation or appraisal of his or
her worth—has no important influence on relationship success,
economic welfare, and health (e.g., Baumeister, Campbell,
Krueger, & Vohs, 2003; Boden, Fergusson, & Horwood, 2008;
Krueger, Vohs, & Baumeister, 2008), other studies suggest that
self-esteem has a significant impact on important life outcomes
(e.g., Swann, Chang-Schneider, & McClarty, 2007, 2008; Trzesni-
ewski et al., 2006). At present, the empirical evidence—which we
review next—is still inconclusive with regard to which, if any, life
domains are affected by self-esteem. Although previous research
has identified numerous correlates of self-esteem, including a
variety of relationship, work, and health factors, this research does
not demonstrate that self-esteem actually predicts change in these
correlates. For example, although self-esteem is concurrently cor-
related with career success (Judge & Bono, 2001), self-esteem
might not predict increases in career success over time. Whether
self-esteem is a cause or consequence (or both) of important life
outcomes is a critical question because a causal effect of self-
esteem implies that improving self-esteem would have a beneficial
effect on the outcomes associated with self-esteem. Thus, if self-
esteem has a causal effect, then this would suggest that the inter-
ventions aimed at increasing self-esteem are worthwhile and likely
to contribute to positive life outcomes and reduce the risk for
maladaptive outcomes (for a detailed discussion of this issue, see
Baumeister et al., 2003). In contrast, if self-esteem is simply a
consequence, or epiphenomenon (Seligman, 1993), of positive life
outcomes, then efforts to boost self-esteem may produce little
concrete benefit, either for the individual or for society.
The present research addresses this gap in the literature by
examining effects of self-esteem on life-span trajectories of rela-
tionship satisfaction, job satisfaction, occupational status, salary,
affect, depression, and health, using data from a large longitudinal
study of four generations of individuals ages 16 to 97 years.
Currently, the field lacks a broad theoretical perspective that could
provide a framework for the present research. By examining pat-
terns of findings across developmental contexts (adolescence to
old age), we hope to contribute to building a new, overarching
theory of the causes and consequences of self-esteem across the
life course.
This article was published Online First September 26, 2011.
Ulrich Orth, Department of Psychology, University of Basel, Basel,
Switzerland; Richard W. Robins and Keith F. Widaman, Department of
Psychology, University of California, Davis.
This research was supported by Swiss National Science Foundation
Grant PP00P1-123370 to Ulrich Orth.
Correspondence concerning this article should be addressed to Ulrich
Orth, Department of Psychology, University of Basel, Missionsstrasse 62,
4055 Basel, Switzerland. E-mail: ulrich.orth@unibas.ch
Journal of Personality and Social Psychology, 2012, Vol. 102, No. 6, 1271–1288
© 2011 American Psychological Association 0022-3514/12/$12.00 DOI: 10.1037/a0025558
1271
In addition, we examined the life-span trajectory of self-esteem,
so we can better understand how age-related changes in self-
esteem correspond to age changes in life outcomes. The available
evidence suggests that self-esteem follows a quadratic trajectory
from adolescence to old age, increasing during young and middle
adulthood, reaching a peak at about age 60 and declining in old age
(Orth, Trzesniewski, & Robins, 2010). However, this trajectory
has been documented in only one longitudinal data set (Orth et al.,
2010; Shaw, Liang, & Krause, 2010). A meta-analysis of mean-
level changes in self-esteem suggests that self-esteem increases in
young adulthood and does not change after age 30 years; however,
few longitudinal studies were available after age 30, possibly
limiting the statistical power of analyses in midlife and old age
(Huang, 2010). Thus, further research is needed to replicate the
finding, particularly the old age decline, in other longitudinal
samples.
Relations Between Self-Esteem and Life Outcomes
In this section, we review the available evidence on whether
self-esteem influences relationship satisfaction, job satisfaction,
occupational status, salary, affect, depression, and health and,
conversely, whether self-esteem is influenced by these variables.
Because one goal of the present research is to examine the influ-
ence of self-esteem on the life-span trajectories of the outcomes,
we also briefly review research on age changes in each of the
outcome variables.
Relationship Satisfaction
Cross-sectional studies suggest that self-esteem is positively
correlated with relationship satisfaction (Shackelford, 2001; Voss,
Markiewicz, & Doyle, 1999). This positive relation may arise
because individuals with high self-esteem show more relationship-
enhancing behaviors, whereas individuals with low self-esteem
show more dysfunctional, relationship-damaging behaviors. For
example, individuals with low self-esteem are more sensitive to
rejection and tend to withdraw and reduce interpersonal closeness
following conflicts, thereby undermining satisfaction in close re-
lationships (Murray, Holmes, & Griffin, 2000; Murray, Rose,
Bellavia, Holmes, & Kusche, 2002). Conversely, relationship sat-
isfaction may affect self-esteem. For example, satisfying relation-
ships may increase one’s perceived relational value of oneself and
may thereby positively influence self-esteem (cf. Leary &
Baumeister, 2000). Consistent with this possibility, Andrews and
Brown (1995) found that women who reported becoming closer to
their relationship partner increased in self-esteem over the follow-
ing years.
Some studies have examined the life-span trajectory of relation-
ship satisfaction. In a meta-analysis, age had a small positive effect
on marital satisfaction (Karney & Bradbury, 1995). Consistent
with this finding, Gorchoff, John, and Helson (2008) found that
relationship satisfaction increased over an 18-year period in mid-
dle adulthood. In contrast, Umberson, Williams, Powers, Chen,
and Campbell (2005) found that relationship satisfaction declined
over an 8-year interval in all age groups from young adulthood to
old age. The results of Gilford and Bengtson (1979) suggest that
positive indicators of relationship satisfaction show a U-shaped
relation with age, whereas negative indicators of relationship sat-
isfaction decrease over the life course. Other studies mapped
relationship satisfaction on relationship duration (which is an
imperfect, but useful, proxy for age). Some of these studies re-
ported a U-shaped curve for relationship satisfaction. Specifically,
relationship satisfaction decreased during the first years of a rela-
tionship but increased in later years (Anderson, Russell, &
Schumm, 1983; Orbuch, House, Mero, & Webster, 1996), whereas
other studies found that relationship satisfaction continuously de-
creased across time (e.g., VanLaningham, Johnson, & Amato,
2001) or remained stable in long-married couples (Vaillant &
Vaillant, 1993). In sum, research published to date provides in-
consistent evidence regarding how relationship satisfaction
changes across the life span.
Job Satisfaction
Cross-sectional studies have suggested that self-esteem is pos-
itively related to job satisfaction (Judge & Bono, 2001). The few
available longitudinal studies have suggested that self-esteem pre-
dicts changes in job satisfaction (Judge, Bono, & Locke, 2000;
Judge & Hurst, 2008). However, there is a lack of longitudinal
studies that have controlled for the prior level of job satisfaction
while testing for prospective effects of self-esteem on job satis-
faction; moreover, there is a lack of longitudinal studies that have
tested for possible effects in the opposite direction, that is, whether
job satisfaction predicts changes in self-esteem.
With regard to its life-span trajectory, most studies have re-
ported that job satisfaction increases continuously across adult-
hood (Bernal, Snyder, & McDaniel, 1998; Hunt & Saul, 1975;
Janson & Martin, 1982; Kalleberg & Loscocco, 1983; for meta-
analyses, see Ng & Feldman, 2010, and Rhodes, 1983). For ex-
ample, in a large national probability sample, Bernal, Snyder, and
McDaniel (1998) found a small but significant linear relation
between age and job satisfaction but found no significant quadratic
or cubic trends. However, some studies have found a U-shaped
relation between age and job satisfaction, with the lowest job
satisfaction in middle adulthood (e.g., Clark, Oswald, & Warr,
1996; Hochwarter, Ferris, Perrewe, Witt, & Kiewitz, 2006; Warr,
1992). For example, Clark, Oswald, and Warr (1996) examined
age differences in job satisfaction in a large cross-sectional sample
and found a quadratic, U-shaped relation, even when controlling
for multiple covariates, such as gender, education, ethnicity, and
health. To summarize, the available evidence diverges with regard
to the trajectory from young to middle adulthood but consistently
suggests that job satisfaction increases in the second half of work-
ing life.
Occupational Status and Salary
Self-esteem is positively correlated with occupational status
(Bachman & O’Malley, 1977; Kammeyer-Mueller, Judge, & Pic-
colo, 2008) and salary (Judge, Hurst, & Simon, 2009; Twenge &
Campbell, 2002), which are key indicators of socioeconomic sta-
tus. Occupational status and salary may influence the individual’s
perception of his or her relational value and thereby influence
self-esteem (Leary & Baumeister, 2000). However, it is also plau-
sible that high self-esteem helps the individual to attain higher
education, to be more successful at the workplace, and conse-
quently to gain higher occupational status and salary. At present,
1272 ORTH, ROBINS, AND WIDAMAN
few longitudinal studies have examined prospective relations be-
tween self-esteem and occupational status or salary. Kammeyer-
Mueller et al. (2008) found that self-esteem predicted occupational
status, even when they controlled for prior level of occupational
status. Judge and Hurst (2008) found that positive self-evaluations
predicted higher occupational status and salary from adolescence
to middle adulthood and that the effect became stronger with
increasing age. In the study by Judge et al. (2009), positive
self-evaluations prospectively predicted higher income. Using a
sample of young adults, Salmela-Aro and Nurmi (2007) found that
self-esteem predicted positive career outcomes 10 years later, for
example, whether participants were employed, had a permanent
position, and had a higher salary (they did not control for previous
levels of outcomes). However, we are not aware of previous
studies that tested prospective effects between self-esteem and
occupational status or salary in both directions (i.e., whether self-
esteem predicts occupational status or salary and vice versa) and
simultaneously controlled for prior levels of the variables.
With regard to its life-span trajectory, empirical research sup-
ports the view that occupational status and salary increase from
young adulthood into midlife and then remain high until retirement
(Boyd, 2008; Elstad, 2004; Ganzeboom, De Graaf, Treiman, & de
Leeuw, 1992; Hauser, Sheridan, & Warren, 1999; Helson & Soto,
2005; Judge & Hurst, 2008; Miech, Eaton, & Liang, 2003; Nam &
Boyd, 2004). For example, in a large sample ranging in age from
18 to 72 years, occupational status strongly increased in young
adulthood and reached a peak in midlife (Miech et al., 2003).
Judge and Hurst (2008) examined a large sample of adolescents
and young adults and found that occupational status and pay
strongly increased over the next 25 years.
Affect
Measures of positive and negative affect show strong concurrent
correlations with self-esteem (e.g., Aspinwall & Taylor, 1992;
Joiner, 1995; Watson, Suls, & Haig, 2002). However, to our
knowledge, no previous study has examined prospective relations
between affect and self-esteem. Consequently, we do not know
whether affect predicts self-esteem, self-esteem predicts affect,
both affect and self-esteem predict the other, or neither affect nor
self-esteem predicts the other.
With regard to its life-span trajectory, previous research sug-
gests that positive affect remains relatively stable from young to
middle adulthood (Carstensen, Pasupathi, Mayr, & Nesselroade,
2000; Charles, Reynolds, & Gatz, 2001), possibly increasing in
adulthood (Helson & Soto, 2005; E. M. Kessler & Staudinger,
2009; Löckenhoff, Costa, & Lane, 2008; Mroczek & Kolarz, 1998)
and then slightly decreasing in old age (Charles et al., 2001;
Kunzmann, Little, & Smith, 2000). In contrast, negative affect
decreases from young to middle adulthood (Gross et al., 1997;
Helson & Soto, 2005; E. M. Kessler & Staudinger, 2009; Löck-
enhoff et al., 2008; Mroczek & Kolarz, 1998), but the decrease
levels off in old age (Carstensen et al., 2000; Charles et al., 2001;
Kunzmann et al., 2000). Reviews and theoretical perspectives on
the life-span development of affect have been provided by Charles
(2010), Mroczek (2001), and Scheibe and Carstensen (2010).
Depression
Previous research has suggested that low self-esteem is a risk or
vulnerability factor for depression. That is, low self-esteem pro-
spectively predicts depression, controlling for prior levels of de-
pression (for a review, see Orth, Robins, & Roberts, 2008). The
effect of low self-esteem on depression holds across short time
intervals (e.g., a few days; Metalsky, Joiner, Hardin, & Abramson,
1993; Ralph & Mineka, 1998; Roberts & Monroe, 1992) and
across long intervals (e.g., years; Orth et al., 2008; Trzesniewski et
al., 2006). Moreover, the effect of low self-esteem on depression
holds for men and women and across all age groups from adoles-
cence to old age (Orth, Robins, Trzesniewski, Maes, & Schmitt,
2009). In addition, most prior research has failed to support the
opposite direction of the relation—that low self-esteem is a con-
sequence, rather than a cause, of depression (Ormel, Oldehinkel, &
Vollebergh, 2004; Orth et al., 2008; Orth, Robins, Trzesniewski, et
al., 2009; but see Shahar & Davidson, 2003; Shahar & Henrich,
2010).
With regard to its life-span trajectory, most studies have found
that depression decreases from young adulthood to middle adult-
hood and then increases again in old age, with converging evi-
dence provided by both cross-sectional studies (Gatz & Hurwicz,
1990; R. C. Kessler, Foster, Webster, & House, 1992; Lewinsohn,
Rohde, Seeley, & Fischer, 1991; Miech & Shanahan, 2000; Mi-
rowsky & Ross, 1992; but see Blanchflower & Oswald, 2008) and
longitudinal studies (Davey, Halverson, Zonderman, & Costa,
2004; Kasen, Cohen, Chen, & Castille, 2003; Mirowsky & Kim,
2007; Mirowsky & Reynolds, 2000; Rothermund & Brandtsta¨dter,
2003; Wallace & O’Hara, 1992). For example, R. C. Kessler et al.
(1992) examined two large nationally representative surveys,
which included participants from young adulthood to old age, and
found that age showed a U-shaped quadratic relation with depres-
sion, with the lowest depression levels at about age 50 years in
both samples. In a longitudinal study of older adults ranging from
ages 60 to 96 years depression consistently increased with age
(Davey et al., 2004).
Health
Individuals with high self-esteem tend to report better physical
health (e.g., Benyamini, Leventhal, & Leventhal, 2004; Ma¨kikan-
gas, Kinnunen, & Feldt, 2004). Individuals with high self-esteem
may seek and receive more social support, experience less stress,
and show more adaptive coping behaviors, thereby enhancing their
health. However, it is also plausible that healthy individuals feel
more independent and better able to contribute to their family and
society, which, in turn, would bolster self-esteem. Available lon-
gitudinal data suggest that self-esteem prospectively predicts
health outcomes. For example, Trzesniewski et al. (2006) found
that low self-esteem in adolescence predicted more physical health
problems at age 26 (see also Christie-Mizell, Ida, & Keith, 2010;
Stinson et al., 2008). However, only one study has examined
whether health predicts changes in self-esteem; Reitzes and
Mutran (2006) found that self-esteem and functional health were
reciprocally related across a 2-year interval, controlling for prior
levels in the constructs.
With regard to its life-span trajectory, most studies have sug-
gested that physical health stays relatively constant, or only de-
1273
SELF-ESTEEM DEVELOPMENT AND LIFE OUTCOMES
clines slowly, from adolescence to the 40s or 50s and then worsens
at an accelerating rate into old age. In longitudinal studies, many of
which used national probability samples, researchers have examined
the trajectories of self-rated health (e.g., Liang et al., 2005; Mc-
Cullough & Laurenceau, 2005; Ross & Wu, 1996; Sacker, Worts, &
McDonough, 2011), impairments of functional health, such as being
confined to bed or having difficulties with bathing, dressing, climbing
stairs, or doing heavy housework (e.g., Chiu & Wray, 2010; J. Kim &
Miech, 2009; Liang et al., 2003; Ross & Wu, 1996), physical symp-
toms (e.g., Aldwin, Spiro, Levenson, & Cupertino, 2001), and number
of health problems, such as arthritis, hypertension, stroke, or cancer
(e.g., House et al., 1994).
The Present Research
The goal of the present research was to examine the life-span
development of self-esteem and its effects on important life out-
comes. We used data from a large longitudinal study of individuals
ranging in age from adolescence to old age. We examined the
effects of self-esteem on the development of relationship satisfac-
tion, job satisfaction, occupational status, salary, positive and
negative affect, depression, and health. The cohort-sequential de-
sign, spanning four generations of the same families, allowed us to
determine whether any observed changes were due to intraindi-
vidual change or cohort differences (Baltes, Cornelius, & Nessel-
roade, 1979).
In the first part of this study, we attempted to replicate previous
research suggesting that self-esteem increases across adulthood
and then decreases in old age (Orth et al., 2010). We tested which
of several growth curve models (intercept only, linear, quadratic,
and cubic) yields the best fit to the data. We also used the unique
design of the study to test whether the self-esteem trajectory varied
across generations—children, parents, grandparents, and great-
grandparents. Finally, we examined whether the trajectory varied
as a function of demographic variables (i.e., gender and educa-
tion). In the second part of this study, we examined reciprocal
prospective relations between self-esteem and life outcomes, using
cross-lagged regression analyses. We tested whether any observed
relations held across generations and across the life span. Finally,
in the third part of this study, we examined the influence of
self-esteem on the life-span trajectories of the outcome variables,
using growth curve models with self-esteem as a time-varying
covariate (TVC). For each outcome variable, we also tested
whether the trajectory varied across generations.
As described earlier, we used two types of models (i.e., cross-
lagged regression models and growth curve models with a TVC) to
examine the effects of self-esteem on life outcomes. Each type of
model yields important information that is not provided by the
other type of model. The cross-lagged model tests the direction of
effects between constructs (which is not provided by the growth
curve model with a TVC), because effects are prospectively tested
and autoregressive effects are controlled for. Cross-lagged regres-
sion models are, at present, the most frequently used and recom-
mended models to test whether data are consistent with causal
hypotheses on the relation between constructs, when only nonex-
perimental longitudinal data are available (Cole & Maxwell, 2003;
Finkel, 1995; Little, Preacher, Selig, & Card, 2007). In contrast,
growth curve analyses provide a way to model the developmental
trajectory of a variable and to examine effects of other variables on
the trajectory (which is not provided by cross-lagged models). For
example, it is possible that the cross-lagged regression analysis
shows that self-esteem predicts relative increases in an outcome
variable, whereas the growth curve analysis shows that a strong
negative developmental trend in the outcome outweighs any ben-
eficial effects of self-esteem. Moreover, growth curve models with
a TVC also demonstrate whether the average trajectory of the
outcome is altered when the TVC is controlled for, which cannot
be examined with cross-lagged regression models (Bollen & Cur-
ran, 2006; Grimm, 2007; Preacher, Wichman, MacCallum, &
Briggs, 2008).
The present study extends previous research in several ways.
First, the study examines the development of self-esteem across a
significantly broader age range (i.e., adolescence to old age) than
in previous longitudinal studies, providing a more comprehensive
picture of life-span development of self-esteem. Second, the study
systematically tests for cross-lagged prospective effects between
self-esteem and life outcomes, which helps to clarify the role of
self-esteem as a potential cause or consequence of important
outcomes. For most of the outcomes examined in this research
(i.e., relationship satisfaction, job satisfaction, occupational status,
salary, and positive and negative affect), no previous study has
tested the reciprocal prospective effects in both directions, con-
trolling for prior levels of both constructs. Third, the study exam-
ines the influence of self-esteem on the life-span trajectories of
these outcome variables, allowing us to examine whether the
trajectory of the life outcome is altered when self-esteem is held
constant and to compare the size of the self-esteem effects to
normative changes in the development of life outcomes. Fourth,
the unique design of the study allowed us to test whether the
observed effects replicated across four generations of the same
families, providing important information about the generalizabil-
ity of the findings across generational cohorts. If the results show
that the hypothesized effects of self-esteem on life outcomes are
not confined to specific cohorts but hold across generations, the
findings have important implications for theory and research on
the life-span consequences of self-esteem.
Method
The data come from the Longitudinal Study of Generations
(LSG; Bengtson, 2009). In 1971, three-generation families were
randomly drawn from a subscriber list of about 840,000 members
of a health maintenance organization in Southern California. Since
1991, the study has included a fourth generation (i.e., the great-
grandchildren in the same families). The members of the health
maintenance organization included primarily White working-class
and middle-class families, and very low and very high socioeco-
nomic levels were not represented in the population. However,
level of education among family members corresponded to na-
tional norms at the time the sample was drawn (Bengtson, Biblarz,
& Roberts, 2002). Although the sample was originally recruited in
Southern California, at recent waves, more than half of the sample
lived outside the region in other parts of California, in other states
of the United States or abroad, because of residential mobility of
participants (Bengtson et al., 2002).
Participants were assessed in 1971, 1985, 1988, 1991, 1994,
1997, and 2000. In 1971 and 1985, the LSG did not include the full
self-esteem measure; the present study therefore examines data of
1274 ORTH, ROBINS, AND WIDAMAN
the five waves from 1988 to 2000. We excluded any participant
whose age was unknown or who did not provide data on self-
esteem at any of the five waves.
Participants
The sample included 1,824 individuals (57% female). Table 1
gives an overview of the demographic characteristics for the full
sample and for the four separate generations. The distribution of
gender is relatively even across generations. The age range across
waves was 14 to 102 years; however, because only one assessment
was below age 16 and two assessments were above age 97, we
restricted the analyses to the age range from 16 to 97 years. Of the
participants, 94% were Caucasian, 3% were Hispanic, 1% were
African American, 1% were Native American, and 1% were of
other ethnicity. Because of the low frequencies of ethnicities other
than Caucasian, we did not examine ethnic differences.
Data on study variables were available for 1,448 individuals in
1988, for 1,463 individuals in 1991, for 1,405 individuals in 1994,
for 1,281 individuals in 1997, and for 1,227 individuals in 2000.
To investigate the potential impact of attrition, we compared
individuals who did and did not participate in the most recent wave
of data collection (2000) on study variables assessed at the first
wave (1988). Participants who dropped out versus those who did
not were more likely to be older (Ms52.1 vs. 43.6 years,
respectively; d0.48), were less likely to be female (53% vs.
60%, respectively), had lower levels of education (Ms4.55 vs.
5.26, respectively; d⫽⫺0.45), and reported slightly lower self-
esteem (Ms3.45 vs. 3.51, respectively; d⫽⫺0.13), less
positive affect (Ms0.74 vs. 0.79, respectively; d⫽⫺0.19), less
negative affect (Ms0.31 vs. 0.35, respectively; d⫽⫺0.13),
more depression (Ms1.53 vs. 1.47, respectively; d0.13), and
poorer physical health (Ms2.97 vs. 3.18, respectively; d
0.28); differences in relationship satisfaction, job satisfaction,
occupational status, and salary were all nonsignificant. Although
differences in demographic variables were of medium size, differ-
ences in self-esteem and the life outcome variables were small to
nonsignificant. Thus, nonrepresentativeness because of attrition
was not a serious concern in the present study.
Measures
Self-esteem. Self-esteem was assessed with the 10-item
Rosenberg Self-Esteem Scale (RSE; Rosenberg, 1965), the most
commonly used and well-validated measure of self-esteem (Rob-
ins, Hendin, & Trzesniewski, 2001). At all waves, responses were
measured with a 4-point scale, ranging from 1 (strongly disagree)
to4(strongly agree). However, in 1988 and 1991, the scale labels
of the middle response categories (i.e., 2 somewhat disagree and
3somewhat agree) differed slightly from the labels used in
1994, 1997, and 2000 (i.e., 2 disagree and 3 agree). Conse-
quently, the raw RSE scores (e.g., the means of the RSE items)
were not equivalent across assessments with differing labels and
could not be used for growth curve analyses. Therefore, we com-
puted RSE factor scores by using confirmatory factor analysis with
categorical indicators and equating by common items (these pre-
paratory analyses are reported at the end of the Method section).
The alpha reliability of the RSE was .86 in 1988, .83 in 1991, .86
in 1994, .86 in 1997, and .86 in 2000.
Relationship satisfaction. The LSG uses a 10-item relation-
ship satisfaction scale (Gilford & Bengtson, 1979). Example items
are “you calmly discuss something together,” “you laugh to-
gether,” “you disagree about something important” (reverse
scored), and “one of you becomes critical and belittling” (reverse
scored). Participants reported how frequently they experienced
the situation when they were with their spouse or partner on a
5-point scale (1 hardly ever;2sometimes;3fairly often;
4quite frequently;5almost always), with M3.90 (SD
0.70) averaged across the five waves. The alpha reliability was .87
in 1988, .89 in 1991, .87 in 1994, .89 in 1997, and .88 in 2000.
Job satisfaction. Job satisfaction was assessed using a single
item: “How satisfied would you say you are with your main job?”
Responses were measured on a 5-point scale (1 not at all
satisfied;2not too satisfied;3somewhat satisfied;4very
satisfied;5extremely satisfied), with M3.63 (SD 0.92)
averaged across the five waves.
Occupational status. The LSG provides a measure of occu-
pational status created from the Hauser–Warren Socioeconomic
Index (Hauser & Warren, 1997), which is based on the average
education level and income of persons in each occupation as
reflected in the 1990 U.S. Census. Participants’ scores are based on
information for their current or most recent occupation. Examples
for occupational status scores are 14.9 for farm workers, 29.8 for
mining machine operators, 50.2 for librarians, 64.1 for teachers in
secondary schools, and 77.1 for dentists (for further information,
see Bengtson, 2009, Appendix C1). The measure ranged from 9.6
to 80.5 and had a mean of 43.5 (SD 14.6) averaged across the
five waves.
Salary. Salary was assessed with a 12-point measure, ranging
from 1 (less than $10,000)to12($110,000 or more). The mean
was 4.07 (SD 2.55) averaged across the five waves.
Positive and negative affect. Positive and negative affect
were assessed with the Affect Balance Scale (Bradburn, 1969),
with five items measuring positive affect and five items measuring
negative affect. The validity of the scale has been repeatedly
supported (Harding, 1982; K. A. Kim & Mueller, 2001). Example
items are “particularly excited or interested in something” and
“that things were really going your way” (positive affect) and “so
restless that you couldn’t sit long in a chair” and “bored” (negative
affect). For each item, participants reported whether they ever felt
that way during the past few weeks (0 no;1yes). For positive
affect, the mean was 0.76 (SD 0.26), and for negative affect, the
mean was 0.32 (SD 0.30), averaged across the five waves. In
Table 1
Demographic Characteristics of the Sample
Sample N
Female
(proportion)
Mean age (and SD)
in 1991
a
Age range
across waves
Generation 1 176 .64 83.0 (5.3) 61–97
Generation 2 601 .57 63.3 (5.2) 41–90
Generation 3 851 .56 39.2 (2.7) 19–68
Generation 4 196 .59 20.1 (4.0) 16–47
Full sample 1,824 .57 49.3 (18.3) 16–97
Note. Age is reported in years. The age range is given for observations
with data on study variables.
a
Mean age is reported for 1991 (i.e., the second wave) because Generation
4 individuals were not part of the study in 1988 (i.e., the first wave).
1275
SELF-ESTEEM DEVELOPMENT AND LIFE OUTCOMES
particular, when items are dichotomous, coefficient alpha can
underestimate the reliability of scales. We therefore used the
method by Raykov, Dimitrov, and Asparouhov (2010) for estimat-
ing the reliability of scales with dichotomous items. The estimates
for positive affect were .84 in 1988, .87 in 1991, .84 in 1994, .87
in 1997, and .85 in 2000, and the estimates for negative affect were
.69 in 1988, .69 in 1991, .72 in 1994, .71 in 1997, and .70 in 2000.
Depression. Depression was assessed with the 20-item Cen-
ter for Epidemiologic Studies Depression Scale (CES-D; Radloff,
1977). The CES-D is a frequently used and well-validated measure
for the assessment of depressive symptoms in nonclinical, subclin-
ical, and clinical populations (Eaton, Smith, Ybarra, Muntaner, &
Tien, 2004). For each item, participants reported how frequently
they experienced the symptom during the past week, using a
4-point scale (0 rarely or none of the time;1a little of the
time;2amoderate amount of the time;3most or all of the
time). Because two of the CES-D items were used for equating the
RSE across assessments (further details provided later), these
items were excluded from the CES-D; thus, we used an 18-item
CES-D for the analyses on depression. At each wave, the 18-item
CES-D correlated .99 with the full 20-item CES-D. The 18-item
CES-D had a mean of 1.52 (SD 0.44) averaged across the five
waves and had an alpha reliability of .88 in 1988, .91 in 1991, .90
in 1994, .90 in 1997, and .90 in 2000.
Health. Participants rated their health on a single item:
“Compared to people of your own age, how would you rate your
overall physical health at the present time?” Responses were
measured on a 4-point scale (1 poor;2fair;3good;4
excellent), with M3.07 (SD 0.76) averaged across the five
waves.
Health problems. Health problems were assessed with an
index of 10 dichotomous items. The items were heart problems/
angina, high blood pressure, stroke, cancer, respiratory ailments,
digestive problems, arthritis/rheumatism, diabetes, cataracts/
glaucoma/retinal degeneration, and hearing impairment. The mean
was 1.29 (SD 1.55) averaged across the five waves. Because the
distribution of health problems is positively skewed, with the
largest frequency at zero problems, the variable was used in
logarithmic metric.
1
Education. The LSG includes an 8-point measure of educa-
tion (1 8th grade or less;2some high school, 9th–11th grade;
3high school or vocational school graduate;4specialized
technical, business, or other training after high school;5some
college, 1–3 years;6college or university graduate;7one or
more academic years beyond college, including MA;8post-
graduate degree, PhD, MD, JD, etc.). For most participants, be-
cause of their age, level of education was invariant across waves,
so we used the highest degree that participants reported across
waves as a time-invariant covariate in our analyses. The mean was
5.04 (SD 1.62).
Statistical Analyses
Analyses were conducted with the Mplus 6.1 program (Muthe´n
& Muthe´n, 2010). To deal with missing values, we used full
information maximum likelihood estimation to fit models directly
to the raw data, which produced less biased and more reliable
results compared with conventional methods of dealing with miss-
ing data, such as listwise or pairwise deletion (Allison, 2003;
Schafer & Graham, 2002; Widaman, 2006).
For confirmatory factor models and cross-lagged regression
models, fit was assessed by the comparative fit index (CFI), the
Tucker–Lewis index (TLI), and the root-mean-square error of
approximation (RMSEA), based on the recommendations of Hu
and Bentler (1999) and MacCallum and Austin (2000). Hu and
Bentler suggested that good fit is indicated by values greater than
or equal to .95 for CFI and TLI and by values less than or equal to
.06 for RMSEA. To test for differences in model fit, we used the
test of small differences in fit recommended by MacCallum,
Browne, and Cai (2006, Program C). For these tests, statistical
power was high with values above .99 (MacCallum et al., 2006,
Program D).
For growth curve models, CFI, TLI, and RMSEA were not
available; therefore, the fit of these models was assessed with the
Bayesian information criterion (BIC). For BIC, absolute values
cannot be interpreted, but when comparing models, lower values
indicate better model fit.
Equating the RSE Across Waves
In the LSG, the labels of the RSE response categories were altered
in 1994 (as described earlier), which likely precludes measurement
invariance of the RSE raw scores across waves. Measurement invari-
ance is, however, a basic requirement for growth modeling because
scores at different waves are directly comparable only when measure-
ment invariance holds (Edwards & Wirth, 2009; Widaman, Ferrer, &
Conger, 2010). To resolve this problem, we used confirmatory factor
analysis with categorical indicators (Wirth & Edwards, 2007) and
equating by common items (Edwards & Wirth, 2009) to compute
RSE factor scores. This procedure can establish measurement invari-
ance because all observed scores, by using the common items as
anchors, are mapped on the same latent scale.
To equate by common items, we needed items that were concep-
tually related to self-esteem and that were available at each wave in
identical response format. Two CES-D items met these requirements.
The items were “I felt that I was just as good as other people” (Item
4 of the CES-D) and “I thought my life had been a failure” (reverse
scored; Item 9 of the CES-D). The content of these items suggests that
they are essentially measures of self-esteem. Thus, the self-esteem
measurement models included 10 RSE items and two CES-D items;
all items were available at each wave. For the RSE items, two
different sets of labels for response categories were used (i.e., Set A
in 1988 and 1991 and Set B in 1994, 1997, and 2000). For the CES-D
items, an identical set of labels was used across waves (i.e., Set C). In
a series of increasingly restricted measurement models, we tested the
measurement invariance of the CES-D items. If measurement invari-
ance of the CES-D items holds, then the RSE items can be mapped on
the same scale across waves. In addition, we also tested the measure-
ment invariance of the RSE items for waves using the same set of
labels (i.e., Set A and Set B). Although measurement invariance of the
RSE items is not required for equating by the CES-D items, its
1
We did not compute coefficient alpha for the index of health problems.
Coefficient alpha is not an appropriate measure of reliability for this scale
because health problems are an emergent not latent construct, defined by an
aggregation of relatively independent indicators (Bollen & Lennox, 1991;
Streiner, 2003).
1276 ORTH, ROBINS, AND WIDAMAN
presence would strengthen confidence in the measurement properties
of the RSE.
2
The measurement models included five correlated self-esteem fac-
tors (one factor per wave). In addition, the models included method
factors that accounted for bias due to positive and negative wording of
the items (cf. Marsh, Scalas, & Nagengast, 2010). Both the positive
and negative wording factors were correlated across waves, but pos-
itive wording factors were uncorrelated with negative wording fac-
tors, and all wording factors were uncorrelated with the self-esteem
factors. Including these method factors strongly increased the fit of the
models. Also, the measurement models included longitudinal corre-
lations between the same items measured at different waves (Cole &
Maxwell, 2003). Including these correlations controls for possible
bias due to indicator-specific variance that is not captured by the
self-esteem and wording factors. We analyzed the indicators as cate-
gorical variables, using the mean- and variance-adjusted weighted
least squares (WLSMV) estimator.
The first measurement model (i.e., Model 1) included configural
invariance (Widaman et al., 2010) for both the CES-D and RSE
items. The fit of this model was good (Table 2). Models 2 and 3
tested weak and strong invariance of the CES-D items by progres-
sively constraining the loadings and thresholds, respectively, to be
equal across waves. The fit of these models was as good as the fit
of Model 1, supporting the conclusion that the CES-D items show
measurement invariance and can be used for equating the RSE
factor scores across waves.
3
Models 4 and 5 tested the measure-
ment invariance of the RSE items for waves using the same set of
labels (i.e., Set A and Set B), again by progressively constraining
the loadings and thresholds, respectively, to be equal. The fit of
these models was good (Table 2). Although the RMSEA values
slightly worsened, the difference was small, and the CFI and TLI
values were unaltered. We concluded that, when using the same
response format, the RSE items showed measurement invariance.
4
The RSE factor scores were computed in Model 6, which included
only the RSE items and omitted the CES-D items. The loadings
and thresholds of the RSE items were fixed to the parameter
estimates from Model 5. The participants’ scores on the five latent
self-esteem factors were saved and used as measures of self-
esteem in the remainder of the analyses.
Results
Developmental Trajectory of Self-Esteem
We examined the trajectory of self-esteem from adolescence to old
age, by using growth curve models that capture the development of
self-esteem across the entire observed age range represented in the
sample. Although each participant provided data for, at most, five age
points (covering a 12-year interval), the complete life-span trajectory
was constructed using information from all participants simultane-
ously. This approach is based on the assumption, which is tested later,
that a common trajectory can be modeled across all generations
included in the sample (e.g., Duncan, Duncan, & Strycker, 2006;
Preacher et al., 2008). To account for the fact that the measurement
was asynchronous across age (i.e., the data are organized by waves,
but we were interested in another metric of time, specifically the
individuals’ age at each wave), we used individual slope loadings,
following the recommendations by Mehta and West (2000), Bollen
and Curran (2006), and Preacher et al. (2008).
We estimated a model with an intercept only and linear, qua-
dratic, and cubic growth models (Preacher et al., 2008). Because
the slope loadings are based on age rather than the five measure-
ment occasions, it was possible to estimate relatively complex
trajectories. Age was centered at 50 years. The cubic model had
the best fit to the data (the BIC values were 13,483.5 for the model
with an intercept only; 13,429.4 for the linear model; 13,037.0 for
the quadratic model; and 12,906.6 for the cubic model). Therefore,
2
Although the LSG assessments in 1971 and 1985 (i.e., the two waves
that are not used in the present research) included some items of the RSE,
we did not use these data for the following reasons. In 1971, the LSG used
labels for the RSE response categories that differed from both sets of labels
used in the assessments from 1988 to 2000. Moreover, in 1971 the CES-D
was not included; therefore, no common items were available to equate the
RSE scores from 1971 with scores from any other waves. In 1985, only one
RSE item was used, so the reliability and validity of RSE factor scores for
1985 would have depended on a single item and likely would have been
low. We therefore decided against using data from the 1971 and 1985
assessments of the LSG.
3
Because the chi-square value for the WLSMV cannot be used for
model comparison, we did not formally test for differences in fit of the
measurement models but examined the fit indicators CFI, TLI, and
RMSEA.
4
We also tested the fit of a model that imposed measurement invariance
of the RSE items across all five waves, disregarding the alteration in the
labels of response categories. As expected, the fit of this model (with
CFI .90, TLI .98, and RMSEA .051) was clearly worse than the fit
of the measurement invariance models that accounted for the alteration in
the labels. This result confirms that the difference in response formats
needs to be taken into account in the measurement models.
Table 2
Fit of Self-Esteem Measurement Models With Categorical
Indicators and Equating by Common Items
Model CFI TLI RMSEA
Invariance of CES-D items (with configural
invariance of RSE)
1. Configural invariance .94 .99 .034
2. Weak invariance .95 .99 .033
3. Strong invariance .95 .99 .033
Invariance of RSE (with strong invariance of
CES-D items)
4. Weak invariance .95 .99 .035
5. Strong invariance .95 .99 .036
Computation of RSE factor scores (with CES-D
items excluded)
6. Strong invariance
a
.98 .99 .032
Note. In the LSG, the labels of the RSE response categories were altered
in 1994. Therefore, in a series of measurement models, RSE factor scores
were equated by using two CES-D items that are essentially measures of
self-esteem and that were available at each wave in identical response
format (see text for further explanation). Weak invariance equality
constraints on loadings; strong invariance equality constraints on load-
ings and thresholds. For models with categorical indicators, confidence
intervals of RMSEA are not available. CFI comparative fit index; TLI
Tucker–Lewis index; RMSEA root-mean-square error of approxima-
tion; CES-D Center for Epidemiologic Studies Depression Scale;
RSE Rosenberg Self-Esteem Scale; LSG Longitudinal Study of
Generations.
a
Parameters fixed to estimates of Model 5.
1277
SELF-ESTEEM DEVELOPMENT AND LIFE OUTCOMES
in the remainder of the analyses, we estimated a cubic self-esteem
trajectory. Next, we tested whether there are differences between
generations in the trajectory. Using a multiple-group analysis, we
tested whether a model in which coefficients are freely estimated
yielded a better fit than a model with cross-generation equality
constraints on the coefficients.
5
In these models, the variance of
the cubic growth factor had to be set to zero to allow for conver-
gence of the models. The results showed that a model with con-
straints forcing the same trajectory across generations fit better
than a model without the constraints, suggesting there are no
generational differences in the self-esteem trajectory.
Thus, the evidence suggests that modeling a single coherent tra-
jectory across the observed age range is appropriate. Figure 1A shows
the average predicted trajectory of self-esteem for the full sample.
Overall, self-esteem tended to increase during adolescence, young
adulthood, and middle adulthood, reached a peak at age 51, and then
declined in old age. There was about a one quarter standard deviation
increase (d0.29) from age 16 to 51 and about a two third standard
deviation decrease (d⫽⫺0.67) from age 51 to 97 years. Although the
trajectory shown in Figure 1A appears to be quadratic, it is in fact
cubic. The cubic factor results in a lower increase in young adulthood
and a stronger decrease in old age than would be explained by a
quadratic trajectory. Although one of the turning points of the cubic
trajectory is outside of the observed age range, the cubic factor is
important to fit the trajectory more closely to the data (as indicated by
the better model fit of the cubic trajectory).
Next, we estimated conditional growth curve models (Preacher
et al., 2008) to examine the moderating effects of gender and
education on the self-esteem trajectory. Gender was dummy coded
for these analyses, and education was converted to zscores. For
both gender and education, none of the effects on the intercept or
on the linear, quadratic, and cubic growth factors were significant,
except that education significantly predicted the intercept (the
unstandardized regression coefficient was 0.17; no standardized
estimate is available). We also examined the effects of gender and
education simultaneously: The effects of gender remained nonsig-
nificant, and the education effect on the intercept remained signif-
icant at 0.16. Figure 1B illustrates the education effect by plotting
the predicted self-esteem trajectory for individuals with high (i.e.,
one standard-deviation unit above the mean) and low (i.e., one
standard-deviation unit below the mean) levels of education. As
can be seen, participants with higher levels of education had higher
self-esteem at all ages: The self-esteem difference between partic-
ipants with low versus high education at age 16 corresponded to
d0.38 and at age 97 corresponded to d0.24.
Cross-Lagged Effects of Self-Esteem and
Life Outcomes
Before we examined the effects of self-esteem on the life-span
trajectories of the outcome variables, we first tested whether self-
esteem in fact predicts the outcome variables by using cross-
lagged regression models. Figure 2 provides a generic illustration
of these models (cf. Finkel, 1995). Each model tested the relations
between self-esteem and one of the outcome variables.
6
The rela-
tions between variables were specified as cross-lagged effects,
which indicates the prospective effect of one variable on the other
(e.g., the effect of self-esteem in 1988 on relationship satisfaction
in 1991), after controlling for their stability across time (e.g., the
effect of relationship satisfaction in 1988 on relationship satisfac-
tion in 1991). We accounted for variance due to specific measure-
ment occasions by correlating the residual variances within waves
(e.g., the residual of self-esteem in 1991 and the residual of
relationship satisfaction in 1991; cf. Cole & Maxwell, 2003). The
stability and cross-lagged coefficients were constrained to be equal
across time. Although the fit values were slightly worse than the
normative values specified by Hu and Bentler (1999), we judged
the fit of the models to be overall satisfactory: The CFI values
ranged from .93 to .97, the TLI values ranged from .92 to .96, and
the RMSEA values ranged from .065 to .095. For all outcome
5
In tests of differences between generations, participants from Genera-
tion 4 could not be included because for one of the waves no data were
available (as reported in the Method section, this generation did not
participate in the 1988 assessment). Moreover, in tests of generational
differences for the variables job satisfaction, occupational status, and
salary, participants from Generation 1 could not be included because the
variables were not assessed for this generation (by reasons of study design).
6
The cross-lagged regression analyses for self-esteem and depression
are mostly redundant with analyses reported in a previous study, which
used data from the 1988 to 1997 assessments of the LSG (Orth, Robins,
Trzesniewski, et al., 2009, Study 1). In the present article, we report the
cross-lagged regression analyses for depression for reasons of complete-
ness; however, the growth curve analyses of depression reported in the
remainder of the article have not been published previously.
Figure 1. Average predicted trajectory of self-esteem for the full sample
(Panel A) and for individuals with high (i.e., one standard-deviation unit
above the mean) and low (i.e., one standard-deviation unit below the mean)
levels of education (Panel B). Measures were converted to zscores for the
analysis.
1278 ORTH, ROBINS, AND WIDAMAN
variables, the equality constraints on the stability and cross-lagged
coefficients did not significantly worsen model fit. Using multiple-
group models, we also tested for generational differences in the
stability and cross-lagged coefficients. Importantly, models with
cross-generation equality constraints did not fit significantly worse
than models in which the coefficients were estimated freely.
Table 3 reports the estimates for the stability and cross-lagged
coefficients.
7
The cross-lagged effects showed a consistent picture:
Self-esteem prospectively predicted each of the outcome variables,
whereas the outcome variables generally did not prospectively
predict self-esteem. In all cases, the effect of self-esteem on the
outcome variable was larger than the effect of the outcome vari-
able on self-esteem. The results indicate that individuals with high
self-esteem subsequently reported higher levels of relationship
satisfaction, job satisfaction, occupational status, salary, positive
affect, and health and reported lower levels of negative affect,
depression, and health problems. The multigroup analyses de-
scribed earlier suggest that these effects held across all four gen-
erations. In sum, the results suggest that it is appropriate to
examine the influence of self-esteem on life-span trajectories of
the outcome variables rather than vice versa.
Effect of Self-Esteem on Developmental Trajectories of
Life Outcomes
To establish a baseline trajectory, we first modeled the basic
trajectories of each outcome variable (i.e., without taking self-
esteem into account).
8
As in the growth models for self-esteem, we
used individual slope loadings, based on age, to model the trajec-
tory across the observed age range using information from all
participants simultaneously. For each outcome, we estimated a
model with an intercept only and linear, quadratic, and cubic
growth models. For the analyses, age was centered at 50 years, and
measures were converted to zscores.
9
For salary and health prob-
lems, multigroup analyses indicated statistically significant gener-
ational differences in their trajectories; therefore, it was not ad-
missible to model a single life-span trajectory for these outcomes.
Consequently, we did not include these outcomes in the growth
curve analyses with self-esteem as a TVC. For all other life
outcomes, however, there were no significant generational differ-
ences in their trajectory. For job satisfaction and positive affect,
the linear model had the best fit; for relationship satisfaction and
negative affect, the quadratic model had the best fit; and for
occupational status, depression, and health, the cubic model had
the best fit.
We next examined the effect of self-esteem on the trajectories,
by using growth curve models with a TVC (see Bollen & Curran,
2006; Preacher et al., 2008). Figure 3 shows a generic illustration
of the models, for the case of quadratic growth in the outcome
variable (the models for linear and cubic growth were specified
accordingly). Again, the models included individual slope loadings
7
The standardized coefficients were averaged across time intervals
using Fisher’s Z
r
transformations. Although the coefficients were con-
strained to be equal across time intervals, the constraints were imposed on
unstandardized coefficients (as typically recommended), which led to
slight variation in the resulting standardized coefficients.
8
A previous study examined growth in positive and negative affect
using data from the LSG (Charles et al., 2001). However, the study did not
include any analyses of self-esteem or, specifically, of the influence of
self-esteem on the trajectories of positive and negative affect.
9
For job satisfaction and occupational status, figures show predicted
trajectories from age 16 to age 70 because only a few observations were
available outside of this range.
Figure 2. Cross-lagged regression model of the relations between self-esteem and an outcome variable (Y). The
relations between factors are specified as cross-lagged effects, which indicate the prospective effect of one
variable on the other (e.g., the effect of self-esteem in 1988 on Yin 1991), after controlling for their stability
across time (e.g., the effect of Yin 1988 on Yin 1991). Residual variances (i.e., disturbances) are denoted as d1,
d2, and so forth.
Table 3
Cross-Lagged and Stability Effects of Self-Esteem and
Outcome Variables
Outcome variable (Y)
Cross-lagged effects Stability effects
SE 3YY3SE SE 3SE Y3Y
Relationship satisfaction .05
.01 .85
.72
Job satisfaction .14
.01 .85
.39
Occupational status .03
.00 .85
.76
Salary .03
.01 .85
.83
Positive affect .17
.00 .85
.43
Negative affect .13
.02
.86
.51
Depression .20
.02
.86
.46
Health .11
.02
.85
.59
Health problems .05
.02
.85
.71
Note. The table shows standardized regression coefficients. SE self-
esteem.
p.05.
1279
SELF-ESTEEM DEVELOPMENT AND LIFE OUTCOMES
based on age. At each measurement occasion, the outcome vari-
able is explained simultaneously by growth curve factors and by
repeatedly measured self-esteem (i.e., the TVC). Consequently, the
models provide two types of information on the effects of self-
esteem: The models estimate (a) the concurrent effect of self-
esteem on the outcome while controlling for systematic growth in
the outcome and (b) the expected growth in the outcome when
self-esteem is held constant (Bollen & Curran, 2006; Preacher et
al., 2008).
Table 4 reports the results for the TVC models. To test whether
controlling for self-esteem altered the trajectory of the outcome,
we compared the fit of two models. In one model, the growth
Figure 3. Growth curve model of an outcome variable (Y) with self-esteem as a time-varying covariate, shown
for quadratic growth (the models for linear and cubic growth were specified accordingly). The model captures
the development of Yacross the entire observed age range by using individual slope loadings. Parameters with
individually varying values are represented by diamonds (Mehta & West, 2000; Preacher et al., 2008). Linear
slope loadings at assessments from 1988 to 2000 are denoted as s1 through s5, and quadratic slope loadings are
denoted as q1 through q5. Individual values for these loadings (i.e., age at assessments and the squared values,
respectively; age was centered at 50 years) are included in the analysis through individual data vectors. Residual
variances are denoted as e1 through e5. The model includes covariances between growth factors and the
self-esteem scores at assessments from 1988 to 2000.
Table 4
Effect of Controlling for Self-Esteem on Life-Span Trajectories of Outcome Variables
Outcome variable
BIC
Self-esteem TVC
effect
Model with growth parameters
freely estimated
Model with growth parameters
constrained to basic model
Relationship satisfaction 24,212.2 24,171.5 0.23
Job satisfaction 22,750.7 22,737.8 0.26
Occupational status 21,940.3 21,881.0 0.03
Positive affect 29,868.0 29,887.6 0.38
Negative affect 29,166.4 29,145.6 0.37
Depression 28,707.1 28,765.5 0.52
Health 29,344.2 29,292.1 0.11
Note. For BIC, lower values indicate better model fit. The TVC effects of self-esteem are unstandardized
regression coefficients (standardized coefficients are not available). Values in bold indicate the best fitting
model. BIC Bayesian information criterion; TVC time-varying covariate.
p.05.
1280 ORTH, ROBINS, AND WIDAMAN
parameters were freely estimated, allowing the trajectory to devi-
ate from the basic model for this variable. In the other model, the
growth parameters were fixed to the values from the basic model,
assuming that the trajectory is unaltered by controlling for self-
esteem. For each outcome variable, we selected the model that
better fit the data. The results indicated that controlling for self-
esteem altered the trajectory of positive affect and depression but
did not alter trajectories of the other variables. For the models
selected, we then examined the concurrent TVC effects of self-
esteem (Table 4, right column). All effects were in the expected
direction. The strongest effect of self-esteem emerged for depres-
sion: A one-unit increase in self-esteem corresponded to a decrease
of 0.52 units in the expected level of depression. Medium-sized
effects emerged for positive and negative affect, small to medium-
sized effects emerged for relationship satisfaction and job satis-
faction, and a very small effect emerged for health. The effect of
self-esteem on the trajectory of occupational status was nonsignif-
icant and close to zero.
To examine the results in more detail, we plotted predicted
trajectories (Figure 4). The figure shows the average trajectory for
each outcome variable. If self-esteem had a significant concurrent
TVC effect on the outcome variable—which was the case for all
outcome variables except occupational status—the figure also in-
cludes trajectories for individuals with constantly high and con-
stantly low self-esteem (corresponding to one standard-deviation
unit above and below the mean, respectively). If controlling for
self-esteem altered the expected growth curve of the outcome
variable—which was the case for positive affect and depression—
the figure also includes the controlled trajectory (which is the
expected trajectory for an individual with a constantly average
level of self-esteem).
Several findings shown in the figures merit attention. First,
the average trajectories of the outcome variables are consistent
with the findings reported in the literature, with the exception of
positive affect. In this study, positive affect decreased linearly
from adolescence to old age (with the difference between age
16 and age 97 corresponding to a medium-sized effect, d
0.58). As reviewed in the introduction, previous research
suggests that positive affect is relatively stable across adulthood
and decreases slightly only in old age. As stated, the trajectories
of the other outcome variables converge with findings from
previous studies. Relationship satisfaction showed a U-shaped
trajectory, decreasing slightly until age 46 (d⫽⫺0.23) and
then increasing more strongly into old age (d0.68). Job
satisfaction increased linearly across the observed age range
(i.e., age 16 to age 70; d0.85). Occupational status increased
strongly from age 16 to age 46 (d1.95) and then decreased
slightly (d⫽⫺0.19). Negative affect decreased strongly from
adolescence to middle adulthood and then leveled off in old age
(with the difference between age 16 and age 97 corresponding
to a very strong effect, d⫽⫺1.55). Depression decreased from
adolescence to about age 60 and then increased again into old
age (with the difference between age 16 and age 60 correspond-
ing to a medium-to-strong effect, d⫽⫺0.60, and the increase
between age 60 and age 97 corresponding to a strong effect, d
0.83). Finally, health increased slightly from age 16 to age 46
(d0.09) and then decreased strongly at an accelerating rate
into old age (d⫽⫺0.87).
Second, the figures illustrate the size of the self-esteem effects
on the level of the outcome trajectories (i.e., the concurrent TVC
effects). As mentioned, the largest effects emerged for positive
affect, negative affect, and depression, as illustrated by the dis-
tance between trajectories for individuals with low versus high
self-esteem. In contrast, for relationship satisfaction, job satisfac-
tion, and health, the self-esteem effects on the level of the trajec-
tories were smaller.
Third, for positive affect and depression, the figures illustrate
the effect of controlling for self-esteem on the expected trajectories
(Figures 4D and 4F). Whereas the effect is very small for positive
affect (e.g., at age 97, the difference between the uncontrolled and
controlled trajectory corresponded to d⫽⫺0.03), the effect is
larger for depression (at age 97 the difference corresponded to d
0.29). For depression, controlling for self-esteem attenuated the
decline from adolescence to middle adulthood and also attenuated
the increase from middle adulthood to old age.
Discussion
In the present research, we used data from a large longitudinal
study to investigate the life-span development of self-esteem and
the prospective influence of self-esteem on life outcomes, includ-
ing relationship satisfaction, job satisfaction, occupational status,
salary, positive and negative affect, depression, and health. Several
important findings emerged. First, we replicated the previously
reported curvilinear trajectory of self-esteem (Orth et al., 2010);
specifically, self-esteem increased from adolescence to middle
adulthood, reached a peak at about age 50, and then decreased in
old age. Second, self-esteem had significant cross-lagged effects
on all of the life outcomes examined in the present research, but no
reciprocal effects of life outcomes on self-esteem were found; this
pattern is consistent with the hypothesis that self-esteem is a cause,
rather than a consequence, of life outcomes (Swann et al., 2007;
Trzesniewski et al., 2006). Third, growth curve analyses, with
self-esteem as a TVC, suggested that self-esteem has medium-
sized effects on the life-span trajectories of affect and depression,
small to medium-sized effects on the trajectories of relationship
and job satisfaction, a very small but significant effect on the
trajectory of health, and no significant effect on the trajectory of
occupational status. Moreover, for some of the outcome variables
(specifically, positive affect and depression), self-esteem not only
moderated the level but also the slope of the trajectories. Next, we
discuss each of these findings in more detail.
Implications of the Findings
The present research replicates in an independent sample the
curvilinear trajectory of self-esteem previously found in analyses
of the Americans’ Changing Lives study (Orth et al., 2010; Shaw
et al., 2010). Although the present study suggests an earlier peak
of the self-esteem trajectory (i.e., at about age 50 years) than in
previous research (at about age 60 years; Orth et al., 2010), the
overall shape of the trajectory was similar. The repeated finding of
a relatively strong decline of self-esteem in old age is of particular
importance, given conflicting reviews of the literature (Bengtson,
Reedy, & Gordon, 1985; Demo, 1992), conflicting results from
studies focusing on old age (e.g., Coleman, Ivani-Chalian, &
Robinson, 1993; Gove, Ortega, & Style, 1989; Ranzjin, Keeves,
1281
SELF-ESTEEM DEVELOPMENT AND LIFE OUTCOMES
Figure 4. Predicted trajectories of outcome variables. The figure shows average trajectories (continuous lines),
trajectories for individuals with constantly high self-esteem (long dash-dot lines), trajectories for individuals with
constantly low self-esteem (dashed lines), and—for positive affect (Panel D) and depression (Panel F) for which
the trajectory was altered when self-esteem was included as a time-varying covariate—trajectories for individ-
uals with a constantly average level of self-esteem (dotted lines). High and low self-esteem corresponded to one
standard-deviation unit above and below the mean, respectively. For job satisfaction and occupational status, the
age range is restricted to 16 to 70 years because only a few observations were available outside of this range.
For occupational status, only the average trajectory is shown because self-esteem did not significantly predict the
trajectory of this outcome variable. The measures were converted to zscores for the analyses.
1282 ORTH, ROBINS, AND WIDAMAN
Luszcz, & Feather, 1998; Reitzes, Mutran, & Fernandez, 1996),
and the negative impact of low self-esteem on a person’s general
well-being.
We also investigated possible moderators of the life-span tra-
jectory of self-esteem. Consistent with previous research (Orth et
al., 2010), educational attainment affected the overall level but not
the shape of the trajectory; specifically, the self-esteem trajectory
for more educated individuals was consistently higher than the
trajectory for less educated individuals, but individuals at both
high and low education levels tended to show a curvilinear trend.
Surprisingly, gender did not affect the level or the trajectory of
self-esteem; in contrast, previous research has suggested that men
tend to report higher self-esteem than women, at least in adoles-
cence and adulthood, although the effect size is generally small
(Kling, Hyde, Showers, & Buswell, 1999; Orth et al., 2010;
Robins, Trzesniewski, Tracy, Gosling, & Potter, 2002). Moreover,
in the present study, no cohort differences in the trajectory of
self-esteem were found, replicating findings from Erol and Orth
(2011) and Orth et al. (2010). Thus, although the claim that there
has been a generational increase in self-esteem levels (i.e., that
more recent generations have higher self-esteem than previous
generations) has intuitive appeal (Twenge & Campbell, 2001,
2008), the available evidence suggests that the average self-esteem
trajectory has not changed across the generations born in the 20th
century (Trzesniewski & Donnellan, 2010; Trzesniewski, Donnel-
lan, & Robins, 2008). Finally, it is worth noting that the self-
esteem measure used (i.e., the RSE factor scores) showed strong
longitudinal measurement invariance. This result strengthens con-
fidence in the validity of the growth curve analyses given that
measurement invariance is a fundamental but rarely tested assump-
tion on which growth models are based (Edwards & Wirth, 2009;
Widaman et al., 2010).
The present research also addressed the important question of
whether self-esteem is better thought of as a cause or a conse-
quence of life outcomes. We tested for reciprocal prospective
relations between self-esteem and a set of life outcomes that are
central to having a successful and fulfilling life, including mea-
sures of well-being (positive affect, negative affect, and depres-
sion), enjoying and succeeding in work, having a satisfying ro-
mantic relationship, and physical health. With regard to
depression, we replicated previous studies showing that low self-
esteem prospectively predicts depression but that the effect of
depression on low self-esteem is small or nonsignificant (Metalsky
et al., 1993; Orth, Robins, & Meier, 2009; Orth, Robins, Trzesni-
ewski, et al., 2009; Roberts & Monroe, 1992). A similar pattern
emerged for measures of dispositional positive and negative affect:
Self-esteem predicted increases in positive affect and decreases in
negative affect, controlling for prior levels in the constructs, but
positive affect did not predict subsequent self-esteem, and negative
affect had a statistically significant but small negative effect on
self-esteem. In addition, we found that self-esteem was prospec-
tively related to higher levels of relationship satisfaction, job
satisfaction, occupational status, salary, and physical health, con-
trolling for prior levels of these variables, but none of these life
outcomes had reciprocal effects on self-esteem (or, if significant,
the coefficients were small). Moreover, all results held across
generations. Thus, regardless of whether one was born in the early
1900s or in the 1980s, self-esteem had significant benefits for
people’s experiences of love, work, and health, supporting hypoth-
eses about the beneficial consequences of high self-esteem (Don-
nellan, Trzesniewski, Robins, Moffitt, & Caspi, 2005; Swann et
al., 2007; Trzesniewski et al., 2006; but see Baumeister et al.,
2003). In this context, it is worth noting that the subjective expe-
rience, or phenomenological state, of having high self-esteem and
feeling positive about oneself is an intrinsically desirable end state,
regardless of whether it causes the individual to get better grades,
earn more money, live longer, engage in less crime, or achieve
other objective outcomes.
Finally, we investigated the influence of self-esteem on the
life-span trajectories of life outcomes, given that the cross-lagged
analyses suggested that self-esteem has prospective effects on the
outcome variables but not vice versa. Whereas the cross-lagged
analyses tested whether self-esteem predicts change in the rank-
order position in life outcomes, the growth curve analyses tested
whether self-esteem moderates the life-span trajectory of life out-
comes. The results showed that self-esteem had small to medium-
sized effects on the life-span trajectories of relationship satisfac-
tion, job satisfaction, positive affect, negative affect, and
depression and had a very small effect on the life-span trajectory
of health. In contrast, self-esteem did not significantly predict the
trajectory of a person’s occupational status. As in the growth curve
analyses of self-esteem, we tested for cohort differences in the
life-span trajectories of the outcome variables. For all variables,
results suggested that that any existing cohort differences were too
small to preclude constructing a single overall trajectory from
adolescence to old age. Although some may be surprised that we
found no significant cohort differences for any of the outcome
variables, research in other domains, such as the Big Five person-
ality traits, likewise suggest that cohort differences are typically
small or nonexistent (Terracciano, McCrae, Brant, & Costa, 2005).
Our results allow us to compare the size of the self-esteem
effects to the size of normative changes in the life outcomes across
the life span. For example, the effect of self-esteem on the trajec-
tory of relationship satisfaction was 0.23 (indicating that a one
standard-deviation unit increase in self-esteem predicted a 0.23
standard-deviation unit increase in relationship satisfaction). In
comparison, relationship satisfaction decreased from age 16 to age
46 by 0.23 standard-deviation units and then increased into old age
by 0.68 standard-deviation units. These data indicate that the
self-esteem effect and the normative change from adolescence to
midlife are of comparable size; for example, if an individual
improves his or her rank in self-esteem by one standard-deviation
unit (relative to other individuals in his age group), the resulting
change in relationship satisfaction outweighs the expected norma-
tive loss from age 16 to age 46. In contrast, the average increase in
relationship satisfaction from midlife to old age largely overrides
the moderating effects of self-esteem. Another example is job
satisfaction: The normative change from age 16 to age 70 corre-
sponded to d0.85, whereas the self-esteem effect was 0.26;
thus, although self-esteem can significantly moderate the trajectory
of job satisfaction, it is unlikely that self-esteem would substan-
tially alter the general trend of the job satisfaction trajectory.
Limitations and Future Directions
One limitation is that the sample, although large and economi-
cally diverse, is not representative of the population of the United
States. Therefore, future research should test whether the results
1283
SELF-ESTEEM DEVELOPMENT AND LIFE OUTCOMES
hold in other, ideally nationally representative, samples. Moreover,
future research should examine the effects of self-esteem on the
development of life outcomes in countries from more diverse
cultural contexts, such as Asian and African cultures (cf. Arnett,
2008). For example, individuals from Asian and Western cultures
show different self-construal styles and different tendencies to-
ward self-enhancement (Heine, Lehman, Markus, & Kitayama,
1999; Markus & Kitayama, 1991), which may have important
consequences for the size and even the direction of self-esteem
effects on trajectories of relationship, work, and health outcomes.
Therefore, whether studies with samples from other cultural con-
texts would yield the same results as the present study is currently
unknown.
Although we found—in the second part of the study—
prospective effects of self-esteem on life outcomes while control-
ling for prior levels of the constructs, the study design does not
allow for strong conclusions regarding the causal influence of
self-esteem. As in all passive observational designs, effects be-
tween variables may be caused by third variables that were not
assessed (Finkel, 1995). For example, personality factors, such as
neuroticism, might simultaneously affect both self-esteem and
many of the outcome variables. Therefore, future research should
test theoretically relevant third-variable models that might account
for the relations between self-esteem and life outcomes. Neverthe-
less, the prospective models (i.e., cross-lagged regression models)
are useful because they can indicate whether the data are consistent
with a causal model of the relation between the variables, by
establishing the direction of the effects and ruling out some (but
not all) alternative causal hypotheses. Further evidence on the
causal status of the effects might also accrue from intervention
studies. For example, if improvement of self-esteem through psy-
chological intervention were followed by improvements in rela-
tionship satisfaction, success at the workplace, psychological well-
being, and health, the causal status of self-esteem would be
enhanced. Although we do not yet know whether interventions to
improve self-esteem lead to improvements in the relationship,
work, and health domains, meta-analytic reviews of self-esteem
intervention programs have demonstrated that “it is possible to
significantly improve children’s and adolescents’ levels of [self-
esteem/self-concept] and to obtain concomitant positive changes in
other areas of adjustment” (Haney & Durlak, 1998, p. 429; see also
Marsh & Craven, 2006; O’Mara, Marsh, Craven, & Debus, 2006).
Future research should include informant-based measures and
additional objective measures of life outcomes. This could be
done, for example, in the relationship domain (e.g., partner ratings
of relationship quality), the work domain (e.g., supervisor and peer
ratings of job performance), and the health domain (e.g., measures
of cardiovascular health, immune functioning, etc.). Typically,
correlations between measures that are based on the same method
(e.g., self-report) are artificially inflated by shared method vari-
ance. In the present context, however, shared method variance is
unlikely to account for the cross-lagged effects because some of it
has been statistically removed by controlling for concurrent rela-
tions and prior levels of each construct. Moreover, we included
three objective measures of life outcomes (occupational status,
salary, health problems), which showed the same pattern of results
as the self-report measures. Self-esteem prospectively predicted
the objective outcomes, whereas the prospective effects of the
outcomes on self-esteem were generally nonsignificant. Although
the self-esteem effects were small, they may accumulate to mean-
ingful differences over the course of a person’s life. Or, to frame
it another way, although the effects might be relatively small for
each individual person, the implications at the societal level could
be quite large. For example, the effect of aspirin on heart disease
is .02 (Meyer et al., 2001), yet doctors regularly prescribe aspirin
when people have heart problems because the reduction in risk is
meaningful at the societal level when millions of people are taking
aspirin.
Future research should seek to identify the cognitive, emotional,
behavioral, and social processes that mediate the effects of self-
esteem on life outcomes. These processes will likely differ across
life domains, although some general processes might account for
effects in several life domains. For example, a possible behavioral
pathway is that low self-esteem motivates social avoidance and
withdrawal (e.g., Murray et al., 2000), thereby impeding social
reinforcement and social support (Ottenbreit & Dobson, 2004),
which likely has negative impact on life domains, such as rela-
tionships, work, and health. A possible intrapersonal pathway is
that low self-esteem may increase the tendency to ruminate about
negative aspects of the self (Cambron, Acitelli, & Pettit, 2009;
Luyckx et al., 2008). Rumination, in turn, strengthens negative
affect and depression (Mor & Winquist, 2002; Nolen-Hoeksema,
2000), which may have further detrimental consequences for job
performance, relationship quality, and health.
Finally, in future research, it would be interesting to examine the
effects of self-esteem after controlling for narcissism. Although
self-esteem is only moderately related to narcissism and self-
enhancement, in particular when measured by the RSE (Ackerman
et al., 2011; Brown & Zeigler-Hill, 2004; Kwan, John, Kenny,
Bond, & Robins, 2004; Robins et al., 2001), it is possible that the
effects of self-esteem on life outcomes are even stronger once
narcissistic self-enhancement is controlled for. In addition, it
would be interesting to test whether the effect of self-esteem on
life outcomes is similar for implicit measures of self-esteem.
However, although implicit measures are a promising avenue for
self-esteem measurement, there is not yet sufficiently strong sup-
port for their validity (Buhrmester, Blanton, & Swann, 2011).
In summary, the present research contributes to the understand-
ing of the life-span development of self-esteem and its possible
consequences for important life outcomes. Our findings are con-
sistent with the hypothesis that self-esteem has a significant pro-
spective impact on real-world life experiences and that high and
low self-esteem are not mere epiphenomena of success and failure
in relevant life domains. An important task in future research is to
further test whether self-esteem causally influences well-being and
success in the domains of work, relationships, and health, for
example, by examining the long-term effects of interventions
aimed at increasing self-esteem.
References
Ackerman, R. A., Witt, E. A., Donnellan, M. B., Trzesniewski, K. H.,
Robins, R. W., & Kashy, D. A. (2011). What does the Narcissistic
Personality Inventory really measure? Assessment, 18, 67–87.
Aldwin, C. M., Spiro, A., Levenson, M. R., & Cupertino, A. P. (2001).
Longitudinal findings from the Normative Aging Study: III. Personality,
individual health trajectories, and mortality. Psychology and Aging, 16,
450465.
1284 ORTH, ROBINS, AND WIDAMAN
Allison, P. D. (2003). Missing data techniques for structural equation
modeling. Journal of Abnormal Psychology, 112, 545–557.
Anderson, S. A., Russell, C. S., & Schumm, W. R. (1983). Perceived
marital quality and family life-cycle categories: A further analysis.
Journal of Marriage and the Family, 45, 127–139.
Andrews, B., & Brown, G. W. (1995). Stability and change in low
self-esteem: The role of psychosocial factors. Psychological Medicine,
25, 23–31.
Arnett, J. J. (2008). The neglected 95%: Why American psychology needs
to become less American. American Psychologist, 63, 602–614.
Aspinwall, L. G., & Taylor, S. E. (1992). Modeling cognitive adaptation:
A longitudinal investigation of the impact of individual differences and
coping on college adjustment and performance. Journal of Personality
and Social Psychology, 63, 989–1003.
Bachman, J. G., & O’Malley, P. M. (1977). Self-esteem in young men: A
longitudinal analysis of the impact of educational and occupational
attainment. Journal of Personality and Social Psychology, 35, 365–380.
Baltes, P. B., Cornelius, S. W., & Nesselroade, J. R. (1979). Cohort effects
in developmental psychology. In J. R. Nesselroade & P. B. Baltes (Eds.),
Longitudinal research in the study of behavior and development (pp.
61–87). New York, NY: Academic Press.
Baumeister, R. F., Campbell, J. D., Krueger, J. I., & Vohs, K. D. (2003).
Does high self-esteem cause better performance, interpersonal success,
happiness, or healthier lifestyles? Psychological Science in the Public
Interest, 4, 1–44.
Bengtson, V. L. (2009). Longitudinal Study of Generations, 1971, 1985,
1988, 1991, 1994, 1997, 2000 [Data file and codebook]. Ann Arbor, MI:
Inter-University Consortium for Political and Social Research [Distrib-
utor].
Bengtson, V. L., Biblarz, T. J., & Roberts, R. E. L. (2002). How families
still matter: A longitudinal study of youth in two generations. Cam-
bridge, United Kingdom: Cambridge University Press.
Bengtson, V. L., Reedy, M. N., & Gordon, C. (1985). Aging and self-
conceptions: Personality processes and social contexts. In J. E. Birren &
K. W. Schaie (Eds.), Handbook of the psychology of aging (pp. 544
593). New York, NY: Van Nostrand Reinhold.
Benyamini, Y., Leventhal, H., & Leventhal, E. A. (2004). Self-rated oral
health as an independent predictor of self-rated general health, self-
esteem and life satisfaction. Social Science and Medicine, 59, 1109
1116.
Bernal, D., Snyder, D., & McDaniel, M. (1998). The age and job satisfac-
tion relationship: Does its shape and strength still evade us? Journal of
Gerontology: Psychological Sciences, 53B, P287–P293.
Blanchflower, D. G., & Oswald, A. J. (2008). Is well-being U-shaped over
the life cycle? Social Science and Medicine, 66, 1733–1749.
Boden, J. M., Fergusson, D. M., & Horwood, L. J. (2008). Does adolescent
self-esteem predict later life outcomes? A test of the causal role of
self-esteem. Development and Psychopathology, 20, 319–339.
Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural
equation perspective. Hoboken, NJ: Wiley.
Bollen, K. A., & Lennox, R. (1991). Conventional wisdom on measure-
ment: A structural equation perspective. Psychological Bulletin, 110,
305–314.
Boyd, M. (2008). A socioeconomic scale for Canada: Measuring occupa-
tional status from the census. Canadian Review of Sociology, 45, 51–91.
Bradburn, N. M. (1969). The structure of psychological well-being. Chi-
cago, IL: Aldine.
Brown, R. P., & Zeigler-Hill, V. (2004). Narcissism and the non-
equivalence of self-esteem measures: A matter of dominance? Journal of
Research in Personality, 38, 585–592.
Buhrmester, M. D., Blanton, H., & Swann, W. B. (2011). Implicit self-
esteem: Nature, measurement, and a new way forward. Journal of
Personality and Social Psychology, 100, 365–385.
Cambron, M. J., Acitelli, L. K., & Pettit, J. W. (2009). Explaining gender
differences in depression: An interpersonal contingent self-esteem per-
spective. Sex Roles, 61, 751–761.
Carstensen, L. L., Pasupathi, M., Mayr, U., & Nesselroade, J. R. (2000).
Emotional experience in everyday life across the adult life span. Journal
of Personality and Social Psychology, 79, 644655.
Charles, S. T. (2010). Strength and vulnerability integration: A model of
emotional well-being across adulthood. Psychological Bulletin, 136,
1068–1091.
Charles, S. T., Reynolds, C. A., & Gatz, M. (2001). Age-related differences
and change in positive and negative affect over 23 years. Journal of
Personality and Social Psychology, 80, 136–151.
Chiu, C. J., & Wray, L. A. (2010). Physical disability trajectories in older
Americans with and without diabetes: The role of age, gender, race or
ethnicity, and education. Gerontologist, 51, 51–63.
Christie-Mizell, C. A., Ida, A. K., & Keith, V. M. (2010). African Amer-
icans and physical health: The consequences of self-esteem and happi-
ness. Journal of Black Studies, 40, 1189–1211.
Clark, A., Oswald, A., & Warr, P. (1996). Is job satisfaction U-shaped
in age? Journal of Occupational and Organizational Psychology, 69,
57–81.
Cole, D. A., & Maxwell, S. E. (2003). Testing mediational models with
longitudinal data: Questions and tips in the use of structural equation
modeling. Journal of Abnormal Psychology, 112, 558–577.
Coleman, P. G., Ivani-Chalian, C., & Robinson, M. (1993). Self-esteem
and its sources: Stability and change in later life. Ageing and Society, 13,
171–192.
Davey, A., Halverson, C. F., Zonderman, A. B., & Costa, P. T. (2004).
Change in depressive symptoms in the Baltimore Longitudinal Study of
Aging. Journal of Gerontology: Psychological Sciences, 59B, P270
P277.
Demo, D. H. (1992). The self-concept over time: Research issues and
directions. Annual Review of Sociology, 18, 303–326.
Donnellan, M. B., Trzesniewski, K. H., Robins, R. W., Moffitt, T. E., &
Caspi, A. (2005). Low self-esteem is related to aggression, antisocial
behavior, and delinquency. Psychological Science, 16, 328–335.
Duncan, T. E., Duncan, S. C., & Strycker, L. A. (2006). An introduction to
latent variable growth curve modeling: Concepts, issues, and applica-
tions. Mahwah, NJ: Erlbaum.
Eaton, W. W., Smith, C., Ybarra, M., Muntaner, C., & Tien, A. (2004).
Center for Epidemiologic Studies Depression Scale: Review and revi-
sion (CESD and CESD-R). In M. E. Maruish (Ed.), The use of psycho-
logical testing for treatment planning and outcomes assessment: Vol. 3.
Instruments for adults (pp. 363–377). Mahwah, NJ: Erlbaum.
Edwards, M. C., & Wirth, R. J. (2009). Measurement and the study of
change. Research in Human Development, 6, 74–96.
Elstad, J. I. (2004). Health and status attainment: Effects of health on
occupational achievement among employed Norwegian men. Acta So-
ciologica, 47, 127–140.
Erol, R. Y., & Orth, U. (2011). Self-esteem development from age 14 to 30
years: A longitudinal study. Journal of Personality and Social Psychol-
ogy, 101, 607–619.
Finkel, S. E. (1995). Causal analysis with panel data. Thousand Oaks, CA:
Sage.
Ganzeboom, H. B. G., De Graaf, P. M., Treiman, D. J., & de Leeuw, J.
(1992). A standard international socio-economic index of occupational
status. Social Science Research, 21, 1–56.
Gatz, M., & Hurwicz, M. L. (1990). Are old people more depressed?
Cross-sectional data on Center for Epidemiological Studies Depression
Scale factors. Psychology and Aging, 5, 284–290.
Gilford, R., & Bengtson, V. (1979). Measuring marital satisfaction in three
generations: Positive and negative dimensions. Journal of Marriage and
the Family, 41, 387–398.
Gorchoff, S. M., John, O. P., & Helson, R. (2008). Contextualizing change
1285
SELF-ESTEEM DEVELOPMENT AND LIFE OUTCOMES
in marital satisfaction during middle age: An 18-year longitudinal study.
Psychological Science, 19, 1194–1200.
Gove, W. R., Ortega, S. T., & Style, C. B. (1989). The maturational and
role perspectives on aging and self through the adult years: An empirical
evaluation. American Journal of Sociology, 94, 1117–1145.
Grimm, K. J. (2007). Multivariate longitudinal methods for studying de-
velopmental relationships between depression and academic achieve-
ment. International Journal of Behavioral Development, 31, 328–339.
Gross, J. J., Carstensen, L. L., Pasupathi, M., Tsai, J., Skorpen, C. G., &
Hsu, A. Y. C. (1997). Emotion and aging: Experience, expression, and
control. Psychology and Aging, 12, 590–599.
Haney, P., & Durlak, J. A. (1998). Changing self-esteem in children and
adolescents: A meta-analytic review. Journal of Clinical Child Psychol-
ogy, 27, 423–433.
Harding, S. D. (1982). Psychological well-being in Great Britain: An
evaluation of the Bradburn Affect Balance Scale. Personality and Indi-
vidual Differences, 3, 167–175.
Hauser, R. M., Sheridan, J. T., & Warren, J. R. (1999). Socioeconomic
achievements of siblings in the life course: New findings from the
Wisconsin Longitudinal Study. Research on Aging, 21, 338–378.
Hauser, R. M., & Warren, J. R. (1997). Socioeconomic indexes for occu-
pations: A review, update, and critique. Sociological Methodology, 27,
177–298.
Heine, S. J., Lehman, D. R., Markus, H. R., & Kitayama, S. (1999). Is there
a universal need for positive self-regard? Psychological Review, 106,
766–794.
Helson, R., & Soto, C. J. (2005). Up and down in middle age: Monotonic
and nonmonotonic changes in roles, status, and personality. Journal of
Personality and Social Psychology, 89, 194–204.
Hochwarter, W. A., Ferris, G. R., Perrewe, P. L., Witt, L. A., & Kiewitz,
C. (2006). A note on the nonlinearity of the age–job-satisfaction rela-
tionship. Journal of Applied Social Psychology, 31, 1223–1237.
House, J. S., Lepkowski, J. M., Kinney, A. M., Mero, R. P., Kessler, R. C.,
& Herzog, A. R. (1994). The social stratification of aging and health.
Journal of Health and Social Behavior, 35, 213–234.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance
structure analysis: Conventional criteria versus new alternatives. Struc-
tural Equation Modeling, 6, 1–55.
Huang, C. (2010). Mean-level change in self-esteem from childhood
through adulthood: Meta-analysis of longitudinal studies. Review of
General Psychology, 14, 251–260.
Hunt, J. W., & Saul, P. N. (1975). The relationship of age, tenure, and job
satisfaction in males and females. Academy of Management Journal, 18,
690–702.
Janson, P., & Martin, J. K. (1982). Job satisfaction and age: A test of two
views. Social Forces, 60, 1089–1102.
Joiner, T. E. (1995). The price of soliciting and receiving negative feed-
back: Self-verification theory as a vulnerability to depression theory.
Journal of Abnormal Psychology, 104, 364–372.
Judge, T. A., & Bono, J. E. (2001). Relationship of core self-evaluations
traits—self-esteem, generalized self-efficacy, locus of control, and emo-
tional stability—with job satisfaction and job performance: A meta-
analysis. Journal of Applied Psychology, 86, 80–92.
Judge, T. A., Bono, J. E., & Locke, E. A. (2000). Personality and job
satisfaction: The mediating role of job characteristics. Journal of Applied
Psychology, 85, 237–249.
Judge, T. A., & Hurst, C. (2008). How the rich (and happy) get richer (and
happier): Relationship of core self-evaluations to trajectories in attaining
work success. Journal of Applied Psychology, 93, 849863.
Judge, T. A., Hurst, C., & Simon, L. S. (2009). Does it pay to be smart,
attractive, or confident (or all three)? Relationships among general
mental ability, physical attractiveness, core self-evaluations, and in-
come. Journal of Applied Psychology, 94, 742–755.
Kalleberg, A. L., & Loscocco, K. A. (1983). Aging, values, and rewards:
Explaining age differences in job satisfaction. American Sociological
Review, 48, 78–90.
Kammeyer-Mueller, J. D., Judge, T. A., & Piccolo, R. F. (2008). Self-
esteem and extrinsic career success: Test of a dynamic model. Applied
Psychology: An International Review, 57, 204–224.
Karney, B. R., & Bradbury, T. N. (1995). The longitudinal course of
marital quality and stability: A review of theory, method, and research.
Psychological Bulletin, 118, 3–34.
Kasen, S., Cohen, P., Chen, H., & Castille, D. (2003). Depression in adult
women: Age changes and cohort effects. American Journal of Public
Health, 93, 2061–2066.
Kessler, E. M., & Staudinger, U. M. (2009). Affective experience in
adulthood and old age: The role of affective arousal and perceived affect
regulation. Psychology and Aging, 24, 349–362.
Kessler, R. C., Foster, C., Webster, P. S., & House, J. S. (1992). The
relationship between age and depressive symptoms in two national
surveys. Psychology and Aging, 7, 119–126.
Kim, J., & Miech, R. (2009). The Black–White difference in age trajec-
tories of functional health over the life course. Social Science and
Medicine, 68, 717–725.
Kim, K. A., & Mueller, D. J. (2001). To balance or not to balance:
Confirmatory factor analysis of the Affect-Balance Scale. Journal of
Happiness Studies, 2, 289–306.
Kling, K. C., Hyde, J. S., Showers, C. J., & Buswell, B. N. (1999). Gender
differences in self-esteem: A meta-analysis. Psychological Bulletin, 125,
470–500.
Krueger, J. I., Vohs, K. D., & Baumeister, R. F. (2008). Is the allure of
self-esteem a mirage after all? American Psychologist, 63, 6465.
Kunzmann, U., Little, T. D., & Smith, J. (2000). Is age-related stability of
subjective well-being a paradox? Cross-sectional and longitudinal evi-
dence from the Berlin Aging Study. Psychology and Aging, 15, 511–
526.
Kwan, V. S. Y., John, O. P., Kenny, D. A., Bond, M. H., & Robins, R. W.
(2004). Reconceptualizing individual differences in self-enhancement
bias: An interpersonal approach. Psychological Review, 111, 94–110.
Leary, M. R., & Baumeister, R. F. (2000). The nature and function of
self-esteem: Sociometer theory. In M. P. Zanna (Ed.), Advances in
experimental social psychology (Vol. 32, pp. 1–62). San Diego, CA:
Academic Press.
Lewinsohn, P. M., Rohde, P., Seeley, J. R., & Fischer, S. A. (1991). Age
and depression: Unique and shared effects. Psychology and Aging, 6,
247–260.
Liang, J., Shaw, B. A., Krause, N. M., Bennett, J. M., Blaum, C., Ko-
bayashi, E., . . . Sugisawa, H. (2003). Changes in functional status
among older adults in Japan: Successful and usual aging. Psychology
and Aging, 18, 684695.
Liang, J., Shaw, B. A., Krause, N. M., Bennett, J. M., Kobayashi, E.,
Fukaya, T., & Sugihara, Y. (2005). How does self-assessed health
change with age? A study of older adults in Japan. Journal of Geron-
tology: Social Sciences, 60B, S224–S232.
Little, T. D., Preacher, K. J., Selig, J. P., & Card, N. A. (2007). New
developments in latent variable panel analyses of longitudinal data.
International Journal of Behavioral Development, 31, 357–365.
Löckenhoff, C. E., Costa, P. T., & Lane, R. D. (2008). Age differences in
descriptions of emotional experiences in oneself and others. Journal of
Gerontology: Psychological Sciences, 63B, P92–P99.
Luyckx, K., Schwartz, S. J., Berzonsky, M. D., Soenens, B., Vansteenkiste,
M., Smits, I., & Goossens, L. (2008). Capturing ruminative exploration:
Extending the four-dimensional model of identity formation in late
adolescence. Journal of Research in Personality, 42, 5882.
MacCallum, R. C., & Austin, J. T. (2000). Applications of structural
equation modeling in psychological research. Annual Review of Psychol-
ogy, 51, 201–226.
MacCallum, R. C., Browne, M. W., & Cai, L. (2006). Testing differences
1286 ORTH, ROBINS, AND WIDAMAN
between nested covariance structure models: Power analysis and null
hypotheses. Psychological Methods, 11, 19–35.
Ma¨kikangas, A., Kinnunen, U., & Feldt, T. (2004). Self-esteem, disposi-
tional optimism, and health: Evidence from cross-lagged data on em-
ployees. Journal of Research in Personality, 38, 556–575.
Markus, H. R., & Kitayama, S. (1991). Culture and the self: Implications
for cognition, emotion, and motivation. Psychological Review, 98, 224
253.
Marsh, H. W., & Craven, R. G. (2006). Reciprocal effects of self-concept
and performance from a multidimensional perspective: Beyond seduc-
tive pleasure and unidimensional perspectives. Perspectives on Psycho-
logical Science, 1, 133–163.
Marsh, H. W., Scalas, L. F., & Nagengast, B. (2010). Longitudinal tests of
competing factor structures for the Rosenberg Self-Esteem Scale: Traits,
ephemeral artifacts, and stable response styles. Psychological Assess-
ment, 22, 366–381.
McCullough, M. E., & Laurenceau, J. P. (2005). Religiousness and the
trajectory of self-rated health across adulthood. Personality and Social
Psychology Bulletin, 31, 560–573.
Mehta, P. D., & West, S. G. (2000). Putting the individual back into
individual growth curves. Psychological Methods, 5, 23–43.
Metalsky, G. I., Joiner, T. E., Hardin, T. S., & Abramson, L. Y. (1993).
Depressive reactions to failure in a naturalistic setting: A test of the
hopelessness and self-esteem theories of depression. Journal of Abnor-
mal Psychology, 102, 101–109.
Meyer, G. J., Finn, S. E., Eyde, L. D., Kay, G. G., Moreland, K. L., Dies,
R. R., . . . Reed, G. M. (2001). Psychological testing and psychological
assessment: A review of evidence and issues. American Psychologist,
56, 128–165.
Miech, R. A., Eaton, W., & Liang, K. Y. (2003). Occupational stratification
over the life course: A comparison of occupational trajectories across
race and gender during the 1980s and 1990s. Work and Occupations, 30,
440473.
Miech, R. A., & Shanahan, M. J. (2000). Socioeconomic status and
depression over the life course. Journal of Health and Social Behavior,
41, 162–176.
Mirowsky, J., & Kim, J. (2007). Graphing age trajectories: Vector graphs,
synthetic and virtual cohort projections, and cross-sectional profiles of
depression. Sociological Methods and Research, 35, 497–541.
Mirowsky, J., & Reynolds, J. R. (2000). Age, depression, and attrition in
the National Survey of Families and Households. Sociological Methods
and Research, 28, 476–504.
Mirowsky, J., & Ross, C. E. (1992). Age and depression. Journal of Health
and Social Behavior, 33, 187–205.
Mor, N., & Winquist, J. (2002). Self-focused attention and negative affect:
A meta-analysis. Psychological Bulletin, 128, 638662.
Mroczek, D. K. (2001). Age and emotion in adulthood. Current Directions
in Psychological Science, 10, 87–90.
Mroczek, D. K., & Kolarz, C. M. (1998). The effect of age on positive and
negative affect: A developmental perspective on happiness. Journal of
Personality and Social Psychology, 75, 1333–1349.
Murray, S. L., Holmes, J. G., & Griffin, D. W. (2000). Self-esteem and the
quest for felt security: How perceived regard regulates attachment pro-
cesses. Journal of Personality and Social Psychology, 78, 478498.
Murray, S. L., Rose, P., Bellavia, G. M., Holmes, J. G., & Kusche, A. G.
(2002). When rejection stings: How self-esteem constrains relationship-
enhancing processes. Journal of Personality and Social Psychology, 83,
556–573.
Muthe´n, L. K., & Muthe´n, B. O. (2010). Mplus user’s guide: Sixth edition.
Los Angeles, CA: Muthe´n & Muthe´n.
Nam, C. B., & Boyd, M. (2004). Occupational status in 2000: Over a
century of census-based measurement. Population Research and Policy
Review, 23, 327–358.
Ng, T. W. H., & Feldman, D. C. (2010). The relationships of age with job
attitudes: A meta-analysis. Personnel Psychology, 63, 677–718.
Nolen-Hoeksema, S. (2000). The role of rumination in depressive disorders
and mixed anxiety/depressive symptoms. Journal of Abnormal Psychol-
ogy, 109, 504–511.
O’Mara, A. J., Marsh, H. W., Craven, R. G., & Debus, R. L. (2006). Do
self-concept interventions make a difference? A synergistic blend of
construct validation and meta-analysis. Educational Psychologist, 41,
181–206.
Orbuch, T. L., House, J. S., Mero, R. P., & Webster, P. S. (1996). Marital
quality over the life course. Social Psychology Quarterly, 59, 162–171.
Ormel, J., Oldehinkel, A. J., & Vollebergh, W. (2004). Vulnerability
before, during, and after a major depressive episode. Archives of General
Psychiatry, 61, 990–996.
Orth, U., Robins, R. W., & Meier, L. L. (2009). Disentangling the effects
of low self-esteem and stressful events on depression: Findings from
three longitudinal studies. Journal of Personality and Social Psychology,
97, 307–321.
Orth, U., Robins, R. W., & Roberts, B. W. (2008). Low self-esteem
prospectively predicts depression in adolescence and young adulthood.
Journal of Personality and Social Psychology, 95, 695–708.
Orth, U., Robins, R. W., Trzesniewski, K. H., Maes, J., & Schmitt, M.
(2009). Low self-esteem is a risk factor for depressive symptoms from
young adulthood to old age. Journal of Abnormal Psychology, 118,
472–478.
Orth, U., Trzesniewski, K. H., & Robins, R. W. (2010). Self-esteem
development from young adulthood to old age: A cohort-sequential
longitudinal study. Journal of Personality and Social Psychology, 98,
645–658.
Ottenbreit, N. D., & Dobson, K. S. (2004). Avoidance and depression: The
construction of the Cognitive–Behavioral Avoidance Scale. Behaviour
Research and Therapy, 42, 293–313.
Preacher, K. J., Wichman, A. L., MacCallum, R. C., & Briggs, N. E.
(2008). Latent growth curve modeling. Los Angeles, CA: Sage.
Radloff, L. S. (1977). The CES-D Scale: A self-report depression scale for
research in the general population. Applied Psychological Measurement,
1, 385–401.
Ralph, J. A., & Mineka, S. (1998). Attributional style and self-esteem: The
prediction of emotional distress following a midterm exam. Journal of
Abnormal Psychology, 107, 203–215.
Ranzjin, R., Keeves, J., Luszcz, M., & Feather, N. T. (1998). The role of
self-perceived usefulness and competence in the self-esteem of elderly
adults: Confirmatory factor analyses of the Bachman revision of Rosen-
berg’s Self-Esteem Scale. Journals of Gerontology: Psychological Sci-
ences and Social Sciences, 53B, P96–P104.
Raykov, T., Dimitrov, D. M., & Asparouhov, T. (2010). Evaluation of
scale reliability with binary measures using latent variable modeling.
Structural Equation Modeling, 17, 265–279.
Reitzes, D. C., & Mutran, E. J. (2006). Self and health: Factors that
encourage self-esteem and functional health. Journal of Gerontology:
Social Sciences, 61B, S44–S51.
Reitzes, D. C., Mutran, E. J., & Fernandez, M. E. (1996). Does retirement
hurt well-being? Factors influencing self-esteem and depression among
retirees and workers. Gerontologist, 36, 649656.
Rhodes, S. R. (1983). Age-related differences in work attitudes and be-
havior: A review and conceptual analysis. Psychological Bulletin, 93,
328–367.
Roberts, J. E., & Monroe, S. M. (1992). Vulnerable self-esteem and
depressive symptoms: Prospective findings comparing three alternative
conceptualizations. Journal of Personality and Social Psychology, 62,
804812.
Robins, R. W., Hendin, H. M., & Trzesniewski, K. H. (2001). Measuring
global self-esteem: Construct validation of a single-item measure and the
1287
SELF-ESTEEM DEVELOPMENT AND LIFE OUTCOMES
Rosenberg Self-Esteem Scale. Personality and Social Psychology Bul-
letin, 27, 151–161.
Robins, R. W., Trzesniewski, K. H., Tracy, J. L., Gosling, S. D., & Potter,
J. (2002). Global self-esteem across the life span. Psychology and Aging,
17, 423–434.
Rosenberg, M. (1965). Society and the adolescent self-image. Princeton,
NJ: Princeton University Press.
Ross, C. E., & Wu, C. L. (1996). Education, age, and the cumulative
advantage in health. Journal of Health and Social Behavior, 37, 104
120.
Rothermund, K., & Brandtsta¨dter, J. (2003). Depression in later life:
Cross-sequential patterns and possible determinants. Psychology and
Aging, 18, 80–90.
Sacker, A., Worts, D., & McDonough, P. (2011). Social influences on
trajectories of self-rated health: Evidence from Britain, Germany, Den-
mark and the USA. Journal of Epidemiology and Community Health, 65,
130–136.
Salmela-Aro, K., & Nurmi, J. E. (2007). Self-esteem during university
studies predicts career characteristics 10 years later. Journal of Voca-
tional Behavior, 70, 463–477.
Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state
of the art. Psychological Methods, 7, 147–177.
Scheibe, S., & Carstensen, L. L. (2010). Emotional aging: Recent findings
and future trends. Journal of Gerontology: Psychological Sciences, 65B,
135–144.
Seligman, M. E. P. (1993). What you can change and what you can’t: The
complete guide to successful self-improvement. New York, NY: Fawcett.
Shackelford, T. K. (2001). Self-esteem in marriage. Personality and Indi-
vidual Differences, 30, 371–390.
Shahar, G., & Davidson, L. (2003). Depressive symptoms erode self-
esteem in severe mental illness: A three-wave, cross-lagged study.
Journal of Consulting and Clinical Psychology, 71, 890–900.
Shahar, G., & Henrich, C. C. (2010). Do depressive symptoms erode
self-esteem in early adolescence? Self and Identity, 9, 403–415.
Shaw, B. A., Liang, J., & Krause, N. (2010). Age and race differences in
the trajectory of self-esteem. Psychology and Aging, 25, 84–94.
Stinson, D. A., Logel, C., Zanna, M. P., Holmes, J. G., Cameron, J. J.,
Wood, J. V., & Spencer, S. J. (2008). The cost of lower self-esteem:
Testing a self- and social-bonds model of health. Journal of Personality
and Social Psychology, 94, 412–428.
Streiner, D. L. (2003). Being inconsistent about consistency: When coef-
ficient alpha does and doesn’t matter. Journal of Personality Assess-
ment, 80, 217–222.
Swann, W. B., Chang-Schneider, C., & McClarty, K. L. (2007). Do
people’s self-views matter? American Psychologist, 62, 84–94.
Swann, W. B., Chang-Schneider, C., & McClarty, K. L. (2008). Yes,
cavalier attitudes can have pernicious consequences. American Journal
of Community Psychology, 63, 65–66.
Terracciano, A., McCrae, R. R., Brant, L. J., & Costa, P. T. (2005).
Hierarchical linear modeling analyses of the NEO-PI-R scales in the
Baltimore Longitudinal Study of Aging. Psychology and Aging, 20,
493–506.
Trzesniewski, K. H., & Donnellan, M. B. (2010). Rethinking “Generation
Me”: A study of cohort effects from 1976–2006. Perspectives on Psy-
chological Science, 5, 58–75.
Trzesniewski, K. H., Donnellan, M. B., Moffitt, T. E., Robins, R. W.,
Poulton, R., & Caspi, A. (2006). Low self-esteem during adolescence
predicts poor health, criminal behavior, and limited economic prospects
during adulthood. Developmental Psychology, 42, 381–390.
Trzesniewski, K. H., Donnellan, M. B., & Robins, R. W. (2008). Do
today’s young people really think they are so extraordinary? An exam-
ination of secular trends in narcissism and self-enhancement. Psycho-
logical Science, 19, 181–188.
Twenge, J. M., & Campbell, W. K. (2001). Age and birth cohort differ-
ences in self-esteem: A cross-temporal meta-analysis. Personality and
Social Psychology Review, 5, 321–344.
Twenge, J. M., & Campbell, W. K. (2002). Self-esteem and socioeconomic
status: A meta-analytic review. Personality and Social Psychology Re-
view, 6, 59–71.
Twenge, J. M., & Campbell, W. K. (2008). Increases in positive self-views
among high school students: Birth cohort changes in anticipated perfor-
mance, self-satisfaction, self-liking, and self-competence. Psychological
Science, 19, 1082–1086.
Umberson, D., Williams, K., Powers, D. A., Chen, M. D., & Campbell,
A. M. (2005). As good as it gets? A life course perspective on marital
quality. Social Forces, 84, 493–511.
Vaillant, C. O., & Vaillant, G. E. (1993). Is the U-curve of marital
satisfaction an illusion? A 40-year study of marriage. Journal of Mar-
riage and the Family, 55, 230–239.
VanLaningham, J., Johnson, D. R., & Amato, P. (2001). Marital happiness,
marital duration, and the U-shaped curve: Evidence from a five-wave
panel study. Social Forces, 78, 1313–1341.
Voss, K., Markiewicz, D., & Doyle, A. B. (1999). Friendship, marriage,
and self-esteem. Journal of Social and Personal Relationships, 16,
103–122.
Wallace, J., & O’Hara, M. W. (1992). Increases in depressive symptom-
atology in the rural elderly: Results from a cross-sectional and longitu-
dinal study. Journal of Abnormal Psychology, 101, 398404.
Warr, P. (1992). Age and occupational well-being. Psychology and Aging,
7, 37–45.
Watson, D., Suls, J., & Haig, J. (2002). Global self-esteem in relation to
structural models of personality and affectivity. Journal of Personality
and Social Psychology, 83, 185–197.
Widaman, K. F. (2006). Missing data: What to do with or without them.
Monographs of the Society for Research in Child Development, 71,
42–64.
Widaman, K. F., Ferrer, E., & Conger, R. D. (2010). Factorial invariance
within longitudinal structural equation models: Measuring the same
construct across time. Child Development Perspectives, 4, 10–18.
Wirth, R. J., & Edwards, M. C. (2007). Item factor analysis: Current
approaches and future directions. Psychological Methods, 12, 58–79.
Received October 17, 2010
Revision received July 21, 2011
Accepted August 22, 2011
1288 ORTH, ROBINS, AND WIDAMAN
... Once adolescents gain self-awareness, autonomy, and a sense of control, they are eager to receive respect and acceptance from others; while self-esteem rapidly develops under these conditions [37][38][39], it is unstable, variable, and easily affected by external factors [37,38]. Subsequently, social roles tend to change for emerging adults, such that their personality traits often develop toward maturity [39][40][41]. As a personality trait, self-esteem also exhibits relatively strong normative growth during emerging adulthood [39,40], which helps individuals address various problems [40,41]. ...
... Subsequently, social roles tend to change for emerging adults, such that their personality traits often develop toward maturity [39][40][41]. As a personality trait, self-esteem also exhibits relatively strong normative growth during emerging adulthood [39,40], which helps individuals address various problems [40,41]. As self-esteem is characterized by dynamic changes that occur from adolescence to adulthood, this study investigated its age-related roles in the relationship between parental autonomy support and mental health. ...
... Subsequently, social roles tend to change for emerging adults, such that their personality traits often develop toward maturity [39][40][41]. As a personality trait, self-esteem also exhibits relatively strong normative growth during emerging adulthood [39,40], which helps individuals address various problems [40,41]. As self-esteem is characterized by dynamic changes that occur from adolescence to adulthood, this study investigated its age-related roles in the relationship between parental autonomy support and mental health. ...
Article
Full-text available
Guided by the dual-factor model and self-determination theory, this study explored the relationship between parental autonomy support and mental health (i.e., life satisfaction and emotional problems) in adolescents and emerging adults, with a focus on the mediating role of self-esteem. We conducted two studies among independent samples in China, including 1617 adolescents aged 10 to 17 years (Mage =12.79, SD = 1.63; 50.7% girls; Study 1) and 1274 emerging adults aged 17 to 26 years (Mage = 20.31, SD = 1.63; 56.6% women; Study 2). All participants completed a set of self-reported questionnaires. The results of both studies validated our hypothesis; specifically, parental autonomy support was positively associated with life satisfaction, but negatively associated with emotional problems (emotional symptoms in Study 1 and depressive symptoms in Study 2). Meanwhile, self-esteem partially mediated the positive relationship between parental autonomy support and life satisfaction (R2 = 0.33 in Study 1; R2 = 0.38 in Study 2), and partially mediated the negative relationship between parental autonomy support and emotional problems (R2 = 0.16 in Study 1; R2 = 0.42 in Study 2). In summary, this suggests that the common antecedents of positive and negative indicators of mental health addressed in this study are prevalent in adolescents and emerging adults. These findings have important implications for preventive and interventional efforts aimed at mental health problems in both demographics.
... Over time, studies conducted concerning self-esteem produced different outcomes. One intriguing finding claimed that self-esteem increased throughout adolescence to middle adulthood, where it begins to decrease as one enters old age (Orth, Robins, & Widaman, 2012). It is necessary to put into consideration confounding factors within these results, like success in one's 'hosen endeavor or family life or deterioration of health (Orth, Trzesniewski, & Robins, 2010). ...
... However, it is essential to consider the impact of gender because the past study did not include gender as a variable of interest. Solely, the adolescence stage showed a general increase in self-esteem as what is exhibited by the results (Orth et al., 2012). It is vital to assess possible variables influencing the increase in self-esteem during adolescence. ...
Article
Full-text available
Parenting styles provide the emotional climate for interaction between parents and children and have a significant impact on the family’s quality of life. School performance is considered as the adolescents’ capacity to interact effectively with the school environment by getting the general point average of their grades in the four quarters of School Year 2018-2019. This paper examined the effects of parenting styles on self-esteem and school performance among the Senior High students of Tubigon, Bohol, Philippines. The study utilized the descriptive normative survey method of research in gathering data through the use of a standardized survey tool in getting the parenting styles and self-esteem of the respondents. Data mining or desk review was conducted in securing the academic performance of the Senior High School of Tubigon, Bohol. Data were processed using averaging, Freeman Halton Test, Kruskal-Wallis test, and Chi-Square. The majority of the 400 respondents yielded similar results in the four parenting styles, first is authoritative in both mothers (52.5 percent) and fathers (46.5 percent). It is followed by the permissive, father (21.5 percent), and mother (17.8 percent). It is followed by ambivalent parenting with fathers (18.8 percent) and mothers (17 percent). The majority (75.5 percent) of the respondents have high self-esteem. Almost a fourth (24.5 percent) had average self-esteem, and no one reflected low self-esteem. Nearly half (45.8 percent) of the total number of respondents had satisfactory school performance, more than a third (36 percent) had an outstanding rating, above a tenth (14 percent) had Very Satisfactory, and very few reflected Fairly Satisfactory (4.3 percent) results. The result of the Freeman-Halton test revealed that there is no statistically significant association between the fathers’ and mothers’ parenting styles and the age groups of the respondents. The Chi-square test revealed that the parenting styles of both the father (X2=7.717, df=3, p<0.10) and the mother (X2 =7.683, df=3, p<0.05) are statistically associated with the sex of the respondents. As to the relationship between self-esteem and academic performance, chi-square revealed a significant result. There is strong evidence of a difference (p-value < 0.05) between the mean ranks of at least one pair of the indicated categories. There is strong evidence that suggests that parenting styles have some bearing on how students perform at school.
... Orth et al. (2009) agree that this group of adolescents are more susceptible emotionally compared with those with a high self-esteem. Hence, it is not surprising and as pointed out by Orth et al (2012); Li et al (2010); Sowislo and Orth (2013); Tetzner et al (2017) that low self-esteem adolescents will have a lowered sense of well-being, experience more external and internal conflicts, physical health problems, and eventually educational failure. ...
... However, average Nigerian workers, have been observed to be confronted with difficulties in planning adequately for retirement, owing to poor remuneration and associated physical, socio-economical and psychological constraints, making the expected retirement outcome to be unclear with concerns for their wellbeing (Bello, 2020). ISSN PRINT 2811-3187 ONLINE 2811-3209 Volume 2 NO 1 2023 The anxiety experienced during this time could interfere with their thoughts and emotions, creating a sense of flight or avoidance in many employees (Nweke, 2016;Bello, 2020); employees with symptoms of retirement anxiety are likely to be less productive, inefficient and displayed poor morale/motivational level in the discharge their duties at work (Arogundade, 2016).The perceived adverse effect of the termination of regular sources of income and its matching decline in living standards and lifestyles characterised the state of their job inefficiency and lack of motivation (Yeung &Zhou, 2017 (Orth et al. 2012), an essential feature of psychological health and a buffer against negative emotions through its protective effect against negative influences (Mann et al., 2004). Low self-esteem is related to severe mental health challenges such as depression, anxiety (Sowislo & Orth, 2013), substance use, theft, and risky behaviour (Alavi, 2011). ...
Article
Full-text available
Retirement is usually considered as a major life event that could change the lives of employees.
... In line with the unidimensional approach, self-esteem has been defined as the individual overall evaluation of personal value and self-worth (Rosenberg, 1965;Smith et al., 2014). On the one hand, previous studies have suggested that adolescents' self-esteem was related to mental health problems, i.e. adolescents with a high self-esteem showed fewer symptoms of anxiety and depression (Guo et al., 2018;Orth et al., 2012), coped better with stressful events (Kocayörük & Şimşek, 2015;Thompson et al., 2016) and experienced less stress (Pierkarska, 2020). In Beck's cognitive model of depression (Beck, 1967), one of the dimensions of the cognitive triad of depression is a negative view of oneself (along with a negative view of the world and the future). ...
Article
Full-text available
Objective: This study examined the protective role of self-esteem and perceived emotional intelligence on mental health problems in Spanish adolescents during COVID-19 pandemic. Design: Participants (N = 139; Mage = 13.83 years, SD = 0.96; 63.8% female) completed measures before the outbreak of COVID-19 (T1) and during the first wave of the pandemic in Spain (T2). Main outcome measures: Participants self-reported emotional intelligence, self-esteem, mental health problems and suicidal behavior. Results: Adolescent mental health problems were equally affected by COVID-19 pandemic according to gender, age and lockdown conditions. Adolescents with low levels of emotional intelligence and self-esteem at T1 showed a significant decrease in self-reported anxiety, depression, stress and suicidal behavior at T2. However, adolescents with average or high levels of emotional intelligence and self-esteem at T1 showed no significant changes in mental health problems at T2. Self-esteem at T1 meditated the relationships between emotional intelligence at T1 (clarity and repair) and emotional symptoms at T2 (depression, anxiety and stress). Furthermore, the relationship between self-esteem and anxiety symptoms was moderated by the number of people living together during COVID-19 lockdown. Conclusion: Our findings highlight the protective role of pre-pandemic development of self-esteem and emotional intelligence in mitigating the impact of COVID-19 outbreak on adolescent mental health during the pandemic.
... Old age is part of late adulthood, which starts from the age of 60 years until almost reaching 120 or 125 years. This is the longest span in the entire period of human development which is 50 to 60 years [45]. In line with the study, it is said that there is a relationship between age and gender with the level of depression in the elderly [46]. ...
Article
Full-text available
The occurrence of COVID-19 has a psychological impact on the elderly which will affect mental health and quality of life. This study aimed to identify the relationship between depression, anxiety, coping strategies with the quality of life of the elderly. This cross sectional study was conducted during the COVID-19 pandemic. Cluster sampling technique was used to select 232 sample. This study employed geriatric depression scale (GDS 15) to measure depression, the geriatric anxiety inventory (GAI) to measure anxiety, brief resilient coping skala (BRCS) to measure coping stratecgies, and the WHOQOOL-BRIEF questionnaire to measure quality of life among the elderly. Data analysis used Multiple Linear Regression statistical test. This study showed that there is a correlation between depression and quality of life (p=0.000), anxiety and quality of life (p=0.000) with coping strategies and quality of life (p=0.027). This study recommended the provision of appropriate psychological interventions to improve and maintain the quality of life among the elderly
Article
Individuals with low self-esteem (LSE) may be devalued, whereas individuals with high self-esteem (HSE) are typically praised in Western society. People readily infer traits based on impressions of self-esteem. Across two studies, we address whether impressions of a hypothetical target person’s self-esteem influence judgments beyond the target’s personality. Results revealed that the target’s self-esteem influenced impressions of personality not only of the target, but of their mother and best friend. Moreover, when the target was portrayed as having LSE compared to HSE, participants made more pessimistic estimates of imagined future experiences with the target, even when the controllability of events varied. Overall, impressions of a target’s self-esteem spread beyond the target, influencing perceptions of their close associates and future events.
Article
This study aimed to examine the longitudinal causal relationship between depression and self‐esteem (SE) in older Koreans and analyze gender differences in this correlation. Participants were 4742 older adults aged ≥65 years in baseline 2018 from the Korea Welfare Panel Study. Depression and SE were measured every year from 2018 to 2021. This study estimated causal relationships using the autoregressive cross‐lagged model and analyzed gender differences using a multigroup approach. The findings indicate that depression and SE in older adults support the reciprocal causal model, but there are no gender differences. The study recommends that both depression and SE should be considered in the design of social work intervention programs for older adults but can disregard gender differences.
Article
Full-text available
Recent scholars have dismissed the utility of self-esteem as well as programs designed to improve it. The authors challenge these contentions on conceptual, methodological, and empirical grounds. They begin by proposing that the scope of recent analyses has been overly narrow and should be broadened to include specific as well as global self-views. Using this conceptualization, the authors place recent critiques in historical context, recalling that similarly skeptical commentaries on global attitudes and traits inspired theorizing and empirical research that subsequently restored faith in the value of both constructs. Specifically, they point to 3 strategies for attaining more optimistic assessments of the predictive validity of selfviews: recognizing the utility of incorporating additional variables in predictive schemes, matching the specificity of predictors and criteria, and using theoretically informed standards for evaluating predictor– criterion relationships. The authors conclude that self-views do matter and that it is worthwhile and important to develop and implement theoretically informed programs to improve them.
Article
Full-text available
We review fundamental issues in one traditional structural equation modeling (SEM) approach to analyzing longitudinal data — cross-lagged panel designs. We then discuss a number of new developments in SEM that are applicable to analyzing panel designs. These issues include setting appropriate scales for latent variables, specifying an appropriate null model, evaluating factorial invariance in an appropriate manner, and examining both direct and indirect (mediated), effects in ways better suited for panel designs. We supplement each topic with discussion intended to enhance conceptual and statistical understanding.
Data
Chapter Data, Program Inputs and Outputs for all LGM Examples in the textbook "An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications, Second Edition". Model specifications are included providing program syntax for Amos, EQS, LISREL, and Mplus software programs. The files are arranged by chapter and include syntax, data, and output files for all examples a particular software program is capable of estimating. The first three chapters (specification of the LGM, LGM and repeated measures ANOVA, and multivariate representations of growth and development) cover the development of the LGM. These are followed by three chapters involving multiple group issues and extensions (analyzing growth in multiple populations, accelerated designs, and multilevel longitudinal approaches), and followed by the chapter on growth mixture modeling, which addresses multiple-group issues from a latent class perspective. The remainder of the book covers 'special topics' (chapters on interrupted time series approaches to LGM analyses, growth modeling with ordered categorical outcomes, Missing data models, a latent variable framework for LGM power analyses and Monte Carlo estimation, and latent growth interaction models). The zipfile is quite large (1MB) since it contains all files for the various software programs.