The Effect of Birth Cohort on Well-Being

Abstract

In the present research, we examined the effects of age, cohort, and time of measurement on well-being across adulthood. Cross-sectional and longitudinal analyses of two independent samples—one with more than 10,000 repeated assessments across 30 years (mean assessments per participant = 4.44, SD = 3.47) and one with nationally representative data—suggested that well-being declines with age. This decline, however, reversed when we controlled for birth cohort. That is, once we accounted for the fact that older cohorts had lower levels of well-being, all cohorts increased in well-being with age relative to their own baseline. Participants tested more recently had higher well-being, but time of measurement, unlike cohort, did not change the shape of the trajectory. Although well-being increased with age for everyone, cohorts that lived through the economic challenges of the early 20th century had lower well-being than those born during more prosperous times.

Keywords

aging well-being

Psychological well-being is associated with what most people typically strive for in life. People with higher levels of well-being, for example, tend to have more successful careers (Graham, Eggers, & Sukhtankar, 2004; Judge & Larsen, 2001), longer-lasting and more-satisfying relationships (Diener, Gohm, Suh, & Oishi, 2000; Stutzer & Frey, 2006), and better physical health (Ong, 2010). Well-being may even contribute to longevity: Happier people tend to live longer (Chida & Steptoe, 2008).

Given the importance of well-being for aging, there is considerable interest in how it changes across adulthood. The relation between well-being and age, however, is complex. Cross-sectional and longitudinal findings have suggested that well-being-related constructs tend to be fairly stable (Diener & Suh, 1998; Kurland, Gill, Patrick, Larson, & Phelan, 2006) or decline slightly across adulthood, with relatively steeper declines in old age (Baird, Lucas, & Donnellan, 2010; Charles, Reynolds, & Gatz, 2001; Gerstorf et al., 2010; Holahan, Holahan, Velasquez, & North, 2008; Ostir, Markides, Peek, & Goodwin, 2001; Stacey & Gatz, 1991). Other studies have found, however, that well-being increases with age (Keyes, Shmotkin, & Ryff, 2002; Mroczek & Kolarz, 1998) and that daily positive emotional experiences increase across adulthood, with a slight decline in old age (Carstensen et al., 2011). Still other research has found scores on well-being-related measures to be U-shaped, with higher levels of well-being at the beginning and end of adulthood than in the middle (Blanchflower & Oswald, 2008; Stone, Schwartz, Broderick, & Deaton, 2010). Thus, a clear picture of how well-being may change with age has yet to emerge.

Changes in well-being with age—like changes in any other psychological variable—may be due to maturational processes that are relatively invariant across time or population, or they may reflect differences in the social milieu that are unique to different cohorts (Twenge & Campbell, 2001). That is, 70-year-olds may be less optimistic than 50-year-olds because of the effect of aging, because they were tested in a different historical time, because they belong to different birth cohorts, or because of a combination of these factors. Any given cohort may have had unique experiences (e.g., differences in economic prosperity, medical care, educational opportunities, nutrition) that shape the way in which individuals from that cohort evaluate their happiness and optimism, experiences that do not color evaluations of other cohorts’ members in the same way. It is impossible to disentangle cohort effects from age effects using cross-sectional data because the two are confounded: A 70-year-old belongs to the same age group and birth cohort. Longitudinal studies, particularly studies that participants entered at different ages and in different years, are necessary to disentangle potential differences across cohorts from normative aging effects.

In the present research, we examined age-related changes in well-being and tested for an effect of cohort using two longitudinal studies, the Baltimore Longitudinal Study of Aging (BLSA) and the National Health and Nutrition Examination Survey (NHANES). Started in 1958, the BLSA is one of the oldest continuing longitudinal studies in the United States. The design of the BLSA offers two crucial attributes for distinguishing cohort effects from age effects. First, there is a substantial range of cohorts and ages in the BLSA: Participants in this sample were born across nearly a full century, from 1885 to 1980, and their age at assessment covers a similarly large range, from 19 to 100 years old. Second, because of ongoing recruitment efforts, there is a fair amount of overlap in age across cohorts. For example, the BLSA includes 60-year-old participants who were born in 1920 and tested in 1980 and 60-year-old participants who were born in 1950 and tested in 2010, and many in between. Thus, in this case, any differences in well-being found across these two groups would be due to secular trends (e.g., cohort, time of measurement) rather than age, because age is invariant. The BLSA sample, however, is highly educated and, thus, effects found may not generalize to other populations. We therefore sought to replicate any effects found in the BLSA with nationally representative data from NHANES.

Method

Samples

BLSA sample

For the current study, we used 2,267 community-dwelling volunteers from the BLSA (47% female, 53% male; 73.8% White, 20.0% Black, 6.2% other ethnicities; mean years of education = 16.46, SD = 2.42). Well-being data were collected between 1979 and 2010 at regularly scheduled visits. Participants’ mean age at the first assessment was 57.90 years (SD = 17.07, range = 19–96 years), and their mean age at the most recent assessment was 68.52 years (SD = 16.34, range = 25–100 years). Participants completed the well-being measure up to 19 times (mean assessments per participant = 4.44, SD = 3.47, range = 1–19) for a total of 10,075 assessments. The mean interval between assessments was 2.46 years (SD = 1.78, range = 4 months–20 years). Participants’ year of birth ranged from 1885 to 1980 (M = 1935, SD = 18.94 years).

NHANES sample

The NHANES is a program of studies designed to assess the health and nutritional status of adults and children in the United States (Centers for Disease Control and Prevention, 1977). We used data from the first wave of NHANES (NHANES I); this sample, assessed between 1971 and 1975, included 3,004 adults who completed the well-being measure. This subsample of the NHANES I group is a nationally representative sample of the U.S. population aged 25 to 74 years at the time of data collection. Most participants (n = 2,284) completed the measure again, on average 8.22 years later (SD = 0.68, range = 6–10). At the first assessment, participants’ mean age was 45.94 years (SD = 13.98, range = 25–74), the sample consisted of 56% women and 44% men (90.3% White, 8.4% Black, and 1.3% other ethnicities), and the average level of education was a high school diploma (education ranged from less than high school to advanced degree). Participants’ year of birth ranged from 1889 to 1950 (M = 1928, SD = 14.00 years). Attrition analyses for both samples can be found in the Supplemental Material available online.

Well-being measure

In both samples, well-being was assessed with a subscale of the Center for Epidemiologic Studies Depression Scale (CES-D; Radloff, 1977). This 20-item scale assesses the frequency of a variety of depressive symptoms during the previous week. Items are rated on a 4-point scale from 0 (rarely or none of the time) to 3 (most or all of the time). The well-being subscale is scored by summing four items that measure the experience of positive emotions and well-being (“I enjoyed life,” “I felt I was just as good as other people,” “I felt hopeful about the future,” and “I was happy”) that are typically reverse-scored into the total scale score (Hertzog, Van Alstine, Usala, Hultsch, & Dixon, 1990; Radloff, 1977). The well-being subscale was only moderately correlated with the other three subscales of the CES-D—depressed affect: r = −.30 (BLSA sample) and r = −.22 (NHANES sample), somatic complaints: r = −.25 (BLSA sample) and r = −.17 (NHANES), and interpersonal problems: r = −.12 (BLSA and NHANES samples). In contrast, the correlations between the depressed-affect and somatic-complaints subscales were .59 in the BLSA sample and .68 in NHANES sample. These correlations suggested that well-being (as assessed by the CES-D) is related to, but not the same as, depression (Stansbury, Ried, & Velozo, 2006). At the first BLSA assessment, the mean well-being score was 9.88 (SD = 3.05), and at the first NHANES assessment, the mean well-being score was 9.20 (SD = 3.27).

Statistical analyses

We examined changes in well-being over time in two ways. First, for comparison with previous cross-sectional studies, we used linear regression to predict the first assessment of well-being from age (linear term) and age-squared (quadratic term). Second, to take advantage of the longitudinal data, we used hierarchical linear modeling (HLM; Raudenbush & Bryk, 2002) to estimate the trajectory of well-being across adulthood. Using HLM software (Version 6; Raudenbush, Bryk, & Congdon, 2004), we fit a quadratic model to test for nonlinear changes across adulthood. We then tested sex, ethnicity, education, year of first assessment (i.e., time of measurement), and year of birth (i.e., cohort) as Level 2 predictors of the intercept and linear slope. We centered age in decades on the grand mean (65.95 years for the BLSA sample, 49.20 years for the NHANES sample) to minimize the correlation between the linear and quadratic terms and facilitate interpretation. Time of measurement was defined as the year of the first well-being assessment for each participant in the BLSA, centered on the mean year (1993).¹ Year of birth was centered on the mean birth year (1935 for the BLSA and 1928 for the NHANES). Additional analyses controlled for antidepressant medication use and disease burden in the BLSA sample (see the Supplemental Material for further details).

Results

BLSA sample

Cross-sectional analysis on the first assessment of well-being from each BLSA participant suggested that older adults had a less positive outlook than did younger and middle-aged adults (b _linear = −0.28, SE = 0.04; b _quadratic = −0.08, SE = 0.02; both ps < .01; Fig. 1a). The longitudinal model also indicated that well-being declined with age, although the decline was not as steep as suggested by the cross-sectional analysis (b _linear = −0.09, SE = 0.03; b _quadratic = −0.04, SE = 0.01, both ps < .01; Table 1); controlling for demographic factors (sex, ethnicity, education) did not appreciably change the shape of the trajectory (b _linear = −0.09, SE = 0.03; b _quadratic = −0.03, SE = 0.01; both ps < .01).

Fig. 1.

Average estimated well-being score in the Baltimore Longitudinal Study of Aging sample as a function of participants’ age. Results are shown separately for analyses in which (a) all birth cohorts were combined and (b) each birth cohort was analyzed separately.

Table 1.

Results of Hierarchical Linear Models Predicting Well-Being from Age in Decades

Sample and model	Residual within-subjects variance (σ²;)	Intercept	Linear slope	Quadratic slope
BLSA
Model 1	3.22	10.30 (0.05)**	−0.09 (0.03)**	−0.04 (0.01)**
Model 2	3.42	10.62 (0.07)**	0.50 (0.04)**	−0.05 (0.03)
Model 2a	3.35	10.78 (0.07)**	0.38 (0.06)**	−0.06 (0.03)
NHANES
Model 1	4.43	9.22 (0.07)**	−0.23 (0.03)**	0.01 (0.02)
Model 2	4.37	9.36 (0.10)**	0.07 (0.10)	0.12 (0.08)

Note: N = 2,267 for the Baltimore Longitudinal Study of Aging (BLSA) sample and N = 3,004 for the National Health and Nutrition Examination Survey (NHANES) sample. Standard errors are shown in parentheses. Model 1 included age and age-squared. Model 2 adjusted for sex, ethnicity, education, and year of birth. Model 2a adjusted for Model 2 covariates and year of first assessment.

p < .01.

In contrast to the other demographic factors, year of birth had a dramatic effect on the estimated trajectory of well-being (Table 2). Specifically, controlling for the effect of cohort reversed the sign of the linear slope from negative (b _linear = −0.09, SE = 0.03, p < .01) to positive (b _linear = 0.38, SE = 0.06, p < .01); the quadratic slope remained negative but was reduced to a trend (b _quadratic = −0.06, SE = 0.03, p = .06). Thus, instead of a decline, well-being increased across adulthood with a slight plateau in old age. Antidepressant use and disease burden had negligible effects on this trajectory.

Table 2.

Effects of Demographic Factors and Secular Trends on the Trajectory of Well-Being

Sample and characteristic	Intercept	Linear slope
BLSA
Sex (female)	0.15 (0.08)	0.02 (0.04)
Ethnicity (Black)	−0.34 (0.12)**	0.08 (0.07)
Ethnicity (other)	−0.80 (0.19)**	−0.03 (0.11)
Education^a	0.05 (0.01)**	0.00 (0.01)
Year of first assessment	0.78 (0.09)**	−0.01 (0.06)
Year of birth	0.61 (0.05)**	−0.02 (0.04)
NHANES
Sex (female)	−0.13 (0.10)	0.03 (0.07)
Ethnicity (Black)	−0.43 (0.18)*	0.22 (0.12)
Ethnicity (other)	−0.31 (0.59)	0.02 (0.39)
Education^a	0.36 (0.04)**	−0.05 (0.03)
Year of birth	0.31 (0.10)**	0.13 (0.09)

Education is coded in years in the BLSA sample and by highest degree obtained in the NHANES sample.

p < .05. **p < .01.

To understand the effect of cohort, we plotted the trajectory of five cohorts, limiting the ages to the range of actual ages of assessment within each cohort (Fig. 1b). In all five cohorts, including the oldest, well-being increased slightly over time. There was, however, a substantial difference in the level of well-being across cohorts; each younger cohort reported a more-positive outlook than did the previous cohort, even when measured at the same age. We also examined the average level of well-being at the first assessment as a function of decade of birth (i.e., average level of well-being by cohort independent of age; see Fig. 2). Of note, cohorts who lived through the Great Depression had progressively lower levels of well-being than those reared during more-prosperous times.

Fig. 2.

Estimated marginal mean well-being in the Baltimore Longitudinal Study of Aging sample as a function of participants’ decade of birth, controlling for age, age-squared, sex, ethnicity, and education.

Table 2 shows the effect of the demographic factors and secular trends on the intercept and linear slope of well-being that may explain some of the significant variability in the intercept and slope variance (both ps < .01). White and more-educated participants generally had higher well-being than did non-White and less-educated participants. In addition, participants who entered the study more recently (year of first assessment) and more-recent cohorts had higher average levels of well-being. Year of first assessment, however, did not have the same effect that cohort did on the sign of the linear trajectory; the dramatic reversal of the trajectory was due to when participants were born, not to when they were tested. In addition, there was no effect of either cohort or year of first assessment on the slope of well-being, which indicated that there was not an Age × Cohort or Age × First Assessment interaction. Finally, this effect of cohort was specific to well-being: After removing the well-being items from the total CES-D scale, there was no effect of cohort on the slope of depressive symptoms, nor was there an effect on the items assessed by the other subscales of the CES-D (see the Supplemental Material for an analysis of depressed affect).

NHANES sample

Although year of birth was more restricted in the NHANES (range 1899 to 1950) than in the BLSA, it still covered a period of substantial change in the United States. Similar to the BLSA findings, results from both the cross-sectional analysis (b _linear = −0.24, SE = 0.04, p < .01; b _quadratic = 0.01, SE = 0.03, n.s.) and longitudinal analysis (Table 1) indicated that well-being declined with age. Replicating the effect in the BLSA, results for the NHANES showed that including year of birth in the model reversed the sign of the trajectory, such that well-being increased rather than decreased across adulthood, although the linear slope did not reach statistical significance (Table 1). Replicating the pattern from the BLSA with nationally representative data, NHANES results showed that the well-being of participants from earlier cohorts was lower than the well-being of later cohorts (Fig. 3). As with the BLSA sample, White and more-educated participants in the NHANES sample generally had higher levels of well-being than did non-White and less-educated participants, there was no Age × Cohort interaction (Table 2), and the cohort effect was specific to well-being.

Fig. 3.

Average estimated well-being score in the National Health and Nutrition Examination Survey sample as a function of participants’ age. Results are shown separately for analyses in which (a) all birth cohorts were combined and (b) each birth cohort was analyzed separately.

Discussion

The present findings suggest that participants’ birth cohort may be one factor that obscures the association between well-being and age. Cross-sectional and longitudinal analyses of two independent samples showed that well-being declined with age. The direction of this trajectory reversed, however, once we controlled for the fact that older cohorts started with lower levels of well-being. That is, relative to their starting point, all of the cohorts increased rather than decreased in well-being with age. Although time of measurement was associated with the mean level of well-being—those who entered the study more recently reported greater well-being—it did not alter the shape of the trajectory as cohort did. The present study is a step toward disentangling cohort, age, and time-of-measurement effects on well-being.

Our analysis of cohort suggests a more positive view of happiness in old age than indicated by the cross-sectional and longitudinal analyses. Although participants were getting happier as they grew older, the effect of age was modest, about 1/10 of a standard deviation from the mean per decade in the BLSA sample. By comparison, the effect of cohort was much larger; there was a difference greater than 1 standard deviation from the mean between cohorts born in the early and in the middle part of the 20th century. Still, the effect of age was not trivial; it was larger than the effect of gender and roughly similar to the effect of education (i.e., the difference between holding a high school diploma and holding a college degree). Thus, older adults maintain and may even improve their emotional well-being despite the inevitable physical and social losses that occur with aging.

When individuals make judgments about their well-being, those judgments reflect more than just an assessment of the individual’s current situation. Along with factors such as personality, life events, and demographic characteristics, the sociocultural environment in which individuals grow up may also contribute to ratings of well-being. In the current study, the level of well-being of cohorts born in the early part of the 20th century, particularly those who lived through the Great Depression, was substantially lower than the level of well-being of cohorts who grew up during more-prosperous times. Such economic troubles can have devastating, lasting effects. For example, the psychological effects of unemployment continue even after reemployment, and well-being may never return to pre-unemployment levels (Lucas, Clark, Georgellis, & Diener, 2004). Similar to this individual-level effect, severe economic upheaval at the national level may reduce levels of well-being, a reduction that may persist even through more-prosperous times. The same process does not appear to be true for depressed affect. In contrast to well-being, the experience of negative emotions may reflect more dispositional or maturational processes that are resistant to early social and environmental influences.

The cohort effect on well-being is particularly striking in the BLSA sample, given the generally high socioeconomic status of these participants at the time of testing. More than 50% of the sample has at least a college education (including the older cohorts), and about 40% hold an advanced degree, whereas less than 10% of the sample has less than a high school education. Thus, many BLSA participants among the older cohorts accomplished their educational goals despite the economic upheaval of the 1930s. But even with this level of educational success, the well-being of cohorts born earlier in the 20th century was stunted compared with the well-being of those born later. This effect, however, was not limited to the highly educated BLSA sample. A similar, albeit less pronounced, effect was apparent among the nationally representative NHANES sample, which suggests a pervasive cohort effect on well-being.

A number of factors may account for the observed cohort effects. Cross-cultural research has indicated that higher income tends to be associated with greater well-being, an association stronger in more-developed countries (Diener, Ng, Harter, & Arora, 2010), and that there are significant mean-level differences in well-being between the rich and the poor (Lucas & Schimmack, 2009). The increase in well-being across the 20th century found in the current research may reflect, in part, the economic prosperity in the United States following World War II. Beyond economic issues, other changes in the United States may have contributed to the increase in well-being. Over the 20th century, life expectancy at birth and at age 65 increased dramatically, whereas infant mortality had a similarly dramatic decline (Kinsella, 1992). Nutritional status has likewise changed dramatically, and the source of disease burden has shifted from primarily acute to primarily chronic (Kinsella, 1992). There have also been substantial changes in social norms and attitudes (related to, for example, marriage, divorce, women in the workplace, parenting). One effect of these economic, medical, and cultural changes may be higher levels of well-being.

The 20th century in the United States was a period marked by rapid progress, with increased longevity, greater educational and economic opportunities, and the advent of social services that ensure a minimum standard of living for the elderly. The greater well-being enjoyed by younger cohorts may be the cumulative effect of both economic prosperity and public programs designed to build a highly educated workforce (e.g., the G.I. Bill of 1944 provided an avenue to education for millions of veterans) and buffer against the setbacks of unemployment (e.g., the Federal Unemployment Tax Act of 1939 created unemployment benefits). As young adults today enter a stagnant workforce, the challenges of high unemployment may have implications for their well-being that long outlast the period of joblessness. Economic turmoil may impede psychological, as well as financial, growth even decades after times get better.

Footnotes

Declaration of Conflicting Interests

The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article.

Funding

This research was supported in part by the Intramural Research Program of the National Institute on Aging, National Institutes of Health.

Supplemental Material

Additional supporting information may be found at

Notes

References

Baird

B. M.

Lucas

R. E.

Donnellan

M. B.

(2010). Life satisfaction across the lifespan: Findings from two nationally representative panel studies. Social Indicators Research, 99, 183–203.

Blanchflower

D. G.

Oswald

A. J.

(2008). Is well-being U-shaped over the life cycle? Social Science & Medicine, 66, 1733–1749.

Carstensen

L. L.

Turan

Scheibe

Ram

Ersner-Hershfield

Samanez-Larkin

G. R.

. . . Nesselroade

J. R.

(2011). Emotional experience improves with age: Evidence based on over 10 years of experience sampling. Psychology and Aging, 26, 21–33.

Centers for Disease Control and Prevention. (1977). National health and nutrition examination survey [Data set]. Retrieved from http://www.cdc.gov/nchs/nhanes/nhanesi.htm

Charles

S. T.

Reynolds

C. A.

Gatz

(2001). Age-related differences and change in positive and negative affect over 23 years. Journal of Personality and Social Psychology, 80, 136–151.

Chida

Steptoe

(2008). Positive psychological well-being and mortality: A quantitative review of prospective observational studies. Psychosomatic Medicine, 70, 741–756.

Diener

Gohm

C. L.

Suh

Oishi

(2000). Similarity of the relations between marital status and subjective well-being across cultures. Journal of Cross-Cultural Psychology, 31, 419–436.

Diener

Harter

Arora

(2010). Wealth and happiness across the world: Material prosperity predicts life evaluation, whereas psychosocial prosperity predicts positive feeling. Journal of Personality and Social Psychology, 99, 52–61.

Diener

Suh

E. M.

(1998). Subjective well-being with age: An international analysis. In Shaie

K. W.

Lawton

M. P.

(Eds.), Annual review of gerontology and geriatrics: Vol. 17. Focus on emotion and adult development (pp. 304–324). New York, NY: Springer.

10.

Gerstorf

Ram

Mayraz

Hidajat

Lindenberger

Wagner

G. G.

Schupp

(2010). Late-life decline in well-being across adulthood in Germany, the United Kingdom, and the United States: Something is seriously wrong at the end of life. Psychology and Aging, 25, 477–485.

11.

Graham

Eggers

Sukhtankar

(2004). Does happiness pay? An exploration based on panel data from Russia. Journal of Economic Behavior & Organization, 55, 319–342.

12.

Hertzog

Van Alstine

Usala

P. D.

Hultsch

D. F.

Dixon

(1990). Measurement properties of the Center for Epidemiological Studies Depression Scale (CES-D) in older populations. Psychological Assessment, 2, 64–72.

13.

Holahan

C. K.

Holahan

C. J.

Velasquez

K. E.

North

R. J.

(2008). Longitudinal change in happiness during aging: The predictive role of positive expectancies. International Journal of Aging & Human Development, 66, 229–241.

14.

Judge

T. A.

Larsen

R. J.

(2001). Dispositional affect and job satisfaction: A review and theoretical extension. Organizational Behavior and Human Decision Processes, 86, 67–98.

15.

Keyes

C. L. M.

Shmotkin

Ryff

C. D.

(2002). Optimizing well-being: The empirical encounter of two traditions. Journal of Personality and Social Psychology, 82, 1007–1022.

16.

Kinsella

K. G.

(1992). Changes in life expectancy 1900–1990. American Journal of Clinical Nutrition, 55, 1196S–1202S.

17.

Kurland

B. F.

Gill

T. M.

Patrick

D. L.

Larson

E. B.

Phelan

E. A.

(2006). Longitudinal change in positive affect in community-dwelling older persons. Journal of the American Geriatrics Society, 54, 1846–1853.

18.

Lucas

R. E.

Clark

A. E.

Georgellis

Diener

(2004). Unemployment alters the set point for life satisfaction. Psychological Science, 15, 8–13.

19.

Lucas

R. E.

Schimmack

(2009). Income and well-being: How big is the gap between the rich and the poor? Journal of Research in Personality, 43, 75–78.

20.

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.

21.

Ong

A. D.

(2010). Pathways linking positive emotion and health in later life. Current Directions in Psychological Science, 19, 358–362.

22.

Ostir

G. V.

Markides

K. S.

Peek

M. K.

Goodwin

J. S.

(2001). The association between emotional well-being and the incidence of stroke in older adults. Psychosomatic Medicine, 63, 210–215.

23.

Radloff

L. S.

(1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measures, 1, 385–401.

24.

Raudenbush

S. W.

Bryk

A. S.

(2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.

25.

Raudenbush

S. W.

Bryk

A. S.

Congdon

(2004). HLM 6 for Windows [Computer software]. Skokie, IL: Scientific Software International.

26.

Stacey

C. A.

Gatz

(1991). Cross-sectional age differences and longitudinal change on the Bradburn Affect Balance Scale. The Journal of Gerontology, 46, P76–P78.

27.

Stansbury

J. P.

Ried

L. D.

Velozo

C. A.

(2006). Unidimensionality and bandwidth in the Center for Epidemiologic Studies Depression (CES-D) scale. Journal of Personality Assessment, 86, 10–22.

28.

Stone

A. A.

Schwartz

J. E.

Broderick

J. E.

Deaton

(2010). A snapshot of the age distribution of psychological well-being in the United States. Proceedings of the National Academy of Sciences, USA, 107, 9985–9990.

29.

Stutzer

Frey

B. S.

(2006). Does marriage make people happy, or do happy people get married? Journal of Socio-Economics, 35, 326–347.

30.

Twenge

J. M.

Campbell

W. K.

(2001). Age and birth cohort differences in self-esteem: A cross-temporal meta-analysis. Personality and Social Psychology Review, 5, 321–344.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.09 MB