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Does childhood general cognitive ability at age 12 predict subjective well-being at age 52?
Journal of Research in Personality 46 (2012) 627–631
Contents lists available at SciVerse ScienceDirect
Journal of Research in Personality journal homepage: www.elsevier.com/locate/jrp
Brief Report
Does childhood general cognitive ability at age 12 predict subjective well-being at age 52? Magda Chmiel a,⇑, Martin Brunner b,1, Ulrich Keller a, Daniela Schalke a, Marius Wrulich a, Romain Martin a a b
Center for Educational Measurement and Applied Cognitive Science (EMACS), University of Luxembourg, L-7220 Walferdange, Luxembourg Freie Universität Berlin Kaiserswerther Straße 16/18 14195 Berlin, Germany
a r t i c l e
i n f o
Article history: Available online 28 June 2012 Keywords: Subjective well-being General cognitive ability Life satisfaction Positive affect Negative affect Domain satisfaction with life Socioeconomic status
a b s t r a c t Drawing on a broad, multidimensional conceptualization of subjective well-being, this study examined the power of childhood general cognitive ability to predict life satisfaction, satisfaction with eight individual life domains, and the frequency of experiencing positive and negative affect in middle adulthood. Data were obtained from a representative Luxembourgish sample (N = 738; 53% female) in a longitudinal study conducted in 1968 and 2008. Childhood general cognitive ability was unrelated to life satisfaction, negatively related to negative affect and satisfaction with free time, and positively related to positive affect and satisfaction with some of the life domains associated with socioeconomic success (i.e. finances, self, housing, work, or health). This predictive power persisted even when childhood socioeconomic status was controlled. Ó 2012 Elsevier Inc. All rights reserved.
1. Introduction Subjective well-being (SWB) is a key life outcome (Diener, 1984). One comprehensive and widely applied conceptualization of SWB, developed by Diener, Suh, Lucas, and Smith (1999), distinguishes four major components of SWB (see e.g. Veenhoven (2009) for some alternative conceptualizations of SWB): (a) Life satisfaction (LS) is a cognitive evaluation of the quality of one’s life as a whole; that is, satisfaction with one’s current life circumstances, one’s past, or one’s outlook on the future. (b) Positive affect (PA) refers to recent occurrences of positive emotions such as joy, contentment, or pride. (c) Negative affect (NA) refers to recent occurrences of unpleasant emotions such as guilt, shame, or sadness. (d) Domain satisfaction refers to the level of contentment with specific domains of one’s life such as work, family, people, free time, health, finances, housing, and the self (the last item is also referred to as self-esteem). The major objective of the present paper is to explore the relation of SWB components to general cognitive ability (GCA). GCA (as measured by intelligence tests) has been repeatedly shown to predict key outcome variables such as education, occupation, finance (socioeconomic status; SES), and health (Gottfredson & Deary,
⇑ Corresponding author. Address: University of Luxembourg, Center for Educational Measurement and Applied Cognitive Science (EMACS), Campus Walferdange, L-7220 Walferdange, Luxembourg. E-mail address: [email protected] (M. Chmiel). 1 The Berlin-Brandenburg Institute for School Quality Improvement (ISQ) Otto-vonSimson-Str. 15 14195 Berlin, Germany. 0092-6566/$ - see front matter Ó 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jrp.2012.06.006
2004; Kuncel, Hezlett, & Ones, 2004; Wrulich et al., in press). Moreover, SES has been found to be positively related to various components of SWB (Howell & Howell, 2008). Thus, given that GCA is related to indicators of SES (i.e. education, finance, and occupation) or variables that are also related to SES (i.e. health), we expected that childhood GCA would be related to the level of SWB components that are related to achieving success in life. Previous research on the relation between GCA and SWB has yielded mixed results. For example, three comprehensive metaanalytic studies that examined the relations between GCA and SWB (Ackerman & Heggestad, 1997; DeNeve & Cooper, 1998; Veenhoven & Choi, 2012) found average correlations between GCA and SWB of around zero. However, although each meta-analysis provided valuable scientific insights, we believe that it may be premature to conclude from these results that the true relations between GCA and all components of SWB are actually zero: (a) Most previous research in this area drew on convenience samples. Hence, these samples very likely captured only a restricted range of the variability of GCA and/or the various components of SWB in the target populations. This in turn may have led to an underestimation of the true relations between GCA and the components of SWB. (b) The previous meta-analyses did not analyze the impact of GCA on individual SWB components separately but rather averaged the results obtained for correlated but distinct components of SWB. Hence, positive and negative correlations between GCA and SWB components may have cancelled each other out, contributing to the finding that the correlations averaged to zero. The major goal of the present paper is to explore the long-term effects of childhood GCA on key components of SWB over a 40-year
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time period. To overcome some of the methodological constraints of previous research, we capitalized on a research design that (a) applied the multidimensional conceptualization of SWB by Diener et al. (1999), (b) drew on a representative adult sample, and (c) controlled for the potentially confounding impact of childhood SES on the SWB components in middle adulthood (see Kahneman, Diener, & Schwarz, 1999). 2. Method 2.1. Sample The data were obtained from the longitudinal MAGRIP project, involving two waves of measurement. In 1968, detailed data on GCA and family background were collected from about half the Luxembourgish student population at the end of primary school when most respondents were in Grade 6 (N = 2,824; M = 11.9 years, SD = 7.2 months; 50.1% male; 84.1% Luxembourgish origin). The second wave of measurement was conducted between November 2008 and February 2009. For budgetary reasons, it was not possible to contact all of the former students for whom addresses were available in 2008 (i.e. 2377 out of 2,824 participants). Thus, a random stratified representative sample of 1,632 persons was drawn from the available addresses. Stratification criteria were (a) place of residence in 1968 and (b) gender. Out of these 1,632 persons, 745 persons took part in the second wave of measurement, 300 were unable to contact, and 587 refused to participate. Out of the 745 participants, 738 (M = 51.8 years; SD = 6.6 months; 47.0% male; 85% Luxembourgish origin) completed a comprehensive questionnaire on SWB. They did not receive any remuneration. Analyses for selection bias showed that the follow-up sample of 738 persons was fairly representative of the original sample, with slightly higher mean childhood GCA (Cohen’s d = 0.20) and childhood SES (d = 0.08). 2.2. Measures The various components of SWB were assessed by valid and reliable measures. (The online supplement contains a complete list of items (Table 1) and a correlation of all SWB components that were represented as latent variables (Table 3)). Specifically, life satisfaction was measured by the widely used Satisfaction with Life Scale (Diener, Emmons, Larsen, & Griffin, 1985; Cronbach’s a = .84). Satisfaction with the domain self (a = .62) was assessed
using four items from the Rosenberg (1965) self-esteem scale. Satisfaction with the remaining seven domains was measured by four items taken from the Fragebogen zur Lebenszufriedenheit (Fahrenberg, Myrtek, Schumacher, & Brähler, 2000), respectively. The reliabilities of the domain satisfaction scale scores ranged from a = .62 for people to a = .85 for work, finances, and free time (see also Table 1 in the online supplemental materials). The frequency of experiencing positive affect (PA) and negative affect (NA) was measured by the German version of the Positive Affect Negative Affect Schedule (PANAS; Krohne, Egloff, Kohlmann, & Tausch, 1996). Participants stated how often they generally experienced a certain emotion. The reliability of each of the scale scores was a = .80. To analyze the relations of GCA to the components of SWB, each individual component was captured by corresponding parcel scores (i.e. a sum score of subsets of items from individual scales; see Little, Cunningham, Shahar, & Widaman, 2002). 2.2.1. General cognitive ability General cognitive ability (GCA) was captured as a latent factor that was measured by four scales from the Leistungsprüfsystem (L-P-S [Performance Test System]; Horn, 1962). The L-P-S is a standardized, objective, comprehensive German intelligence test. The scales are: verbal ability (3 subtests; a = .79), reasoning ability (2 subtests; a = .74), visual–spatial ability (2 subtests; a = .59), and processing speed (2 subtests; a = .36). 2.2.2. Childhood socio-economic status Childhood socio-economic status (SES) was captured as a latent factor defined by three indicators. First, the participants’ fathers’ occupations performed in 1968 (or their last performed occupations) were mapped onto the International Socio-Economic Index of Occupational Status (ISEI) scale (Ganzeboom & Treiman, 1996) with higher values indicating higher occupational prestige. Second, the respondents’ fathers’ trained occupations were again mapped onto the ISEI metric. Third, the fathers’ highest academic qualifications were mapped onto the International Standard Classification of Education (ISCED) scale (UNESCO, 1997) with higher values indicating a higher level of education. 2.2.3. Statistical analyses To explore the relations between childhood GCA and SWB in middle adulthood, we ran two different structural equation models (Fig. 1, Table 1). The standardized regression coefficients obtained for Model 1 indicates the predictive power of childhood GCA for each SWB component. Model 2 tested whether the predictive
Table 1 Prediction of SWB components by childhood general cognitive ability (Model 1) and by childhood general cognitive ability and childhood socioeconomic status (Model 2). DV
Model 1
Model 2
GCA
PA NA FI HE HO SE WO PE FA FT LS
R2
b
95% CI
.21 .11 .13 .11 .10 .09 .07 .01 –.02 –.07 .04
[.11, .31] [ .20, .02] [.05, .22] [.01, .21] [.00, .19] [ .02, .20] [ .03, .16] [ .11, .12] [ .13, .10] [ .15, .02] [ .05, .13]
GCA
.04 .01 .02 .01 .01 .01 .01 .00 .00 .00 .00
SES 95% CI
b .21 .12 .12 .10 .09 .12 .07 .02 .02 .06 .04
[.11, .31] [ .21, .02]. [.03, .21] [ .01, .20] [ .01, .19] [.00, .24] [ .02, .17] [ .10, .14] [ .10, .14] [ .15, .03] [ .06, .13]
R2 95% CI
b .01 .04 06 .03 .01 11 .02 .06 .13 .02 .02
[ [ [ [ [ [ [ [ [ [ [
.10, .07, .03, .07, .09, .24, .13, .17, .26, .11, .08,
.09] .15] .16] .13] .11] .01] .08] .05] –.01] .08] .12]
.04 .01 .02 .01 .01 .02 .00 .00 .02 .00 .00
Note. DV = components of SWB served as dependent variables in the structural equation models; GCA = general cognitive ability; SES = childhood socioeconomic status; PA = positive affect, NA = negative affect; FI = finances, HE = health, HO = housing, SE = self, WO = work; PE = people, FA = family, FT = free time, LS = life satisfaction, b = standardized regression coefficient; CI = confidence interval. The standardized regression coefficients from Model 1 are the same as correlation coefficients and can thus be interpreted as standardized effect size measures.
M. Chmiel et al. / Journal of Research in Personality 46 (2012) 627–631
(a)
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(b)
Fig. 1. Structural equation models representing (a) how childhood general cognitive ability (GCA) predicts SWB components (Model 1) and (b) whether this predictive power remains when childhood socioeconomic status (SES) is controlled for (Model 2).
power of childhood GCA persisted when a potentially confounding individual characteristic—childhood SES as measured at age 12— was controlled. Not all participants provided data for all measures: For the items that concerned all participants, data were missing for between four and 28 of 738 participants; for items that did not apply to all participants (e.g. satisfaction with one’s partner), data were missing for between 46 and 202 of 738 participants). To account for this pattern of missing data, we used the full information maximum likelihood estimation method ‘‘MLR’’ as implemented in Mplus 5.2 for all analyses (Muthén & Muthén, 1998–2006). We used the Comparative Fit Index (CFI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR) to assess the fits of Models 1 and 2.
are presented in Table 1. In Model l, childhood GCA was positively related to the frequency of experiencing PA but negatively related to the frequency of experiencing NA in middle adulthood. It also showed small positive associations with satisfaction with finances, health, housing, self, and work 40 years later. Childhood GCA did not predict the level of satisfaction with family or social life (people); however, it was negatively related to satisfaction with free time. The relation of GCA to LS in middle adulthood was negligible. Crucially, the results obtained for Model 2 demonstrate that the predictive power of GCA ability for each component of SWB persisted even when we controlled for childhood SES (see Table 1).
4. Discussion 3. Results The fits of both models were very good (see Fig. 1), with CFI values larger than .95, and RMSEA and SRMR values smaller than .04 (Hu & Bentler, 1999). Moreover, all latent factors were well defined in both models (see Table 3 in the online supplemental materials). Hence, both models allowed us to reliably address our research question. The results of our analyses regarding the relation between childhood GCA and SWB components in middle adulthood
The major goal of the present paper was to explore the longterm impact of childhood GCA on key components of SWB over a 40-year time period. Our results clearly demonstrate that the conclusion that GCA is not related to SWB needs to be reconsidered. Specifically, we found that people with higher childhood GCA were more satisfied with many domains associated with socioeconomic success in life. Persons with a higher level of childhood GCA also experienced more PA and less NA. Effect sizes for the relations between childhood GCA and various components of SWB ranged
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from .10 to .20 (see results obtained for Model 1 in Table 1). At first glance, these relations may seem small. However, they span a period of 40 years, and can be considered typical in lifespan psychology in which effect sizes tend to range between .10 and .30 (Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007). In sum, the predictive power of childhood GCA to forecast several key components of SWB over a 40-year period can be considered substantial. How can the relations between childhood GCA and SWB be explained? As previous research has shown GCA to be positively related to SES (e.g. Judge, Ilies, & Dimotakis, 2010; Strenze, 2007), it seems plausible that it is their higher SES that leads people with higher childhood GCA to experience higher satisfaction with their finances, housing, and work, as well as with themselves (self; e.g. Kuncel et al., 2004). We also suppose that achieving a higher SES can be one of the reasons for why childhood GCA was positively associated with the frequency of experiencing PA and negatively associated with the frequency of experiencing NA. As people with higher GCA are also more successful in many walks of life (Judge et al., 2010; Kuncel et al., 2004), it is very probable that they face more situations that lead them to experience more PA. Analogically, as they lead more fulfilling and engaged lives and face fewer everyday challenges than their counterparts with lower GCA, they are also less likely to experience negative emotions when faced with challenges. Moreover, they have greater cognitive and financial resources to effectively limit the impact of these challenges. A higher SES is also known to be associated with better health (Judge et al., 2010). Furthermore, as a positive impact of GCA has been demonstrated on both objective and perceived physical and mental health status via better coping mechanisms and better health behavior (Gottfredson & Deary, 2004), it may also result in a better subjective evaluation of one’s health status. Finally, the lack of association between childhood GCA and satisfaction with family and people is also aligned with our proposition that childhood GCA shows stronger associations with satisfaction with life domains that are associated with achieving socioeconomic success in life. We observed a negative association between childhood GCA and satisfaction with free time. Free time has a quantitative (i.e. amount of leisure time) and a qualitative component (i.e. quality of leisure time activities). Although higher SES might be associated with a higher quality of leisure activities, higher SES is typically also associated with more demanding jobs resulting in less leisure time. Thus, the observed negative correlation between GCA and satisfaction with free time might reflect the net effect of evaluating both components. Finally, in line with the findings of previous research (Ackerman & Heggestad, 1997; DeNeve & Cooper, 1998; Veenhoven & Choi, 2012, in our study, childhood GCA was not significantly associated with LS 40 years later. LS is the evaluation of the quality of one’s life as a whole (Diener et al., 1999). A person’s LS level is considered to be the sum of the various facets of SWB (Pavot & Diener, 1993) so that LS represents an amalgam of all SWB components (Chmiel, Brunner, Martin, & Schalke, 2012). Thus, the impact of childhood GCA on LS depends on its impact on each of the individual SWB components. However, these relations can have various vectors and strengths, which may counterbalance one another, leading to a nonsignificant overall relation between childhood GCA and LS.
ated the relations observed between childhood GCA and those SWB components. Given that we examined a representative sample of middle-aged adults and administered measures that have been widely used in previous research on SWB, this is an important finding. Future research (particularly with adult respondents) should therefore apply measures (e.g. ambulatory assessment of moods; see Kahneman et al., 1999) with a higher ceiling in order to capture the variance of key components of SWB more fully. Second, although our measures of satisfaction with various life domains were very detailed, they did not allow us to measure the individual impact of GCA on satisfaction with different aspects within certain domains. For example, satisfaction with work encompasses satisfaction with several facets (e.g. prestige, wage, stress, working hours). Such subfacets may be differently influenced by GCA (see Ganzach & Fried, 2012), which in turn may potentially lead to nonsignificant results when only the global component (e.g. satisfaction with work) is considered in the analyses. In order to detect all such possible influences, future research needs to apply more detailed measures, analyzing various facets within life domains. Third, we presented our results in terms of effect sizes and corresponding confidence intervals. In so doing, we followed the guidelines of the American Psychological Association (Wilkinson, 1999). Although many of the effects observed in the present study can be considered substantial (taking into account the 40-year time span and the potential ceiling effects), the 95% confidence intervals for some of these relations included zero. Hence, within these limits of confidence, we cannot completely rule out the possibility that some of these effects were actually zero. Thus, there is a need for further research that not only draws on representative samples and a comprehensive conceptualization of SWB, but that also uses better measures and/or larger samples in order to yield more accurate effect size estimates.
6. Conclusions Our study analyzed the long-term relations of childhood GCA to SWB from a lifespan perspective in a representative adult sample. Our findings showed that people with higher childhood GCA are more satisfied with many SWB components in middle adulthood (health, finances, housing, self), and that they more frequently experience PA and less frequently experience NA in middle adulthood. Moreover, the study demonstrated the importance of using a multidimensional conceptualization of SWB in research. We hope that future research draws on the present results and the applied methodological approach to examine the differential relations between childhood GCA and individual SWB components.
Acknowledgments This study was supported by a grant from the Luxembourgish Fonds National de la Recherché (VIVRE: Living tomorrow in Luxembourg, Project No. FNR/06/09/18) and a PhD scholarship awarded to the first author by the Fonds National de la Recherché. The authors would also like to thank Boris Egloff for providing the German version of the Positive Affect Negative Affect Schedule measures.
5. Limitations One limitation of our study relates to the measures of SWB applied. First, we used self-report measures, which showed a ceiling effect for most of the SWB components examined (see Figs. 1–11 in the supplementary online material). This effect may have attenu-
Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.jrp.2012.06.006.
M. Chmiel et al. / Journal of Research in Personality 46 (2012) 627–631
References Ackerman, P. L., & Heggestad, E. D. (1997). Intelligence, personality, and interests: Evidence for overlapping traits. Psychological Bulletin, 121(2), 219–245. http:// dx.doi.org/10.1037/0033-2909.121.2.219. Chmiel, M., Brunner, M., Martin, R., & Schalke, D. (2012). Revisiting the structure of subjective well-being in middle-aged adults. Social Indicators Research, 106(1), 109–116. http://dx.doi.org/10.1007/s11205-011-9796-7. DeNeve, K. M., & Cooper, H. (1998). The happy personality: A meta-analysis of 137 personality traits and subjective well-being. Psychological Bulletin, 124(2), 197–229. http://dx.doi.org/10.1037//0033-2909.124.2.197. Diener, E. (1984). Subjective well-being. Psychological Bulletin, 95(3), 542–575. http://dx.doi.org/10.1037/0033-2909.95.3.542. Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satisfaction with life scale. Journal of Personality Assesment, 49(1), 71–75. http://dx.doi.org/10.1207/ s15327752jpa4901_13. Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-being: Three decades of progress. Psychological Bulletin, 125(2), 276–302. http://dx.doi.org/ 10.1037/0033-2909.125.2.276. Fahrenberg, J., Myrtek, M., Schumacher, J., & Brähler, E. (2000). Fragebogen zur Lebenszufriedenheit. Handanweisung. Göttingen, Germany: Hogrefe. Satisfaction with life questionnaire. Manual. Ganzach, Y., & Fried, I. (2012). The role of intelligence in the formation of wellbeing: From job rewards to job satisfaction. Intelligence, 40(4), 333–342. http:// dx.doi.org/10.1016/j.intell.2012.03.004. Ganzeboom, H. B. G., & Treiman, D. J. (1996). Internationally comparable measures of occupational status for the 1988 International Standards Classification of Occupations. Social Science Research, 25(3), 201–239. http://dx.doi.org/10.1006/ ssre.1996.0010. Gottfredson, L. S., & Deary, I. J. (2004). Intelligence predicts health and longevity, but why? Current Directions in Psychological Science, 13(1), 1–4. http://dx.doi.org/ 10.1111/j.0963-7214.2004.01301001.x. Horn, W. (1962). Leistungsprüfsystem L-P-S. Göttingen, Germany: Hogrefe. Performance Test System L-P-S. Howell, R. T., & Howell, C. J. (2008). The relation of economic status to subjective well-being in developing countries: A meta-analysis. Psychological Bulletin, 134(4), 536–560. http://dx.doi.org/10.1037/0033-2909.134.4.536. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. http://dx.doi.org/10.1080/ 10705519909540118. Judge, T. A., Ilies, R., & Dimotakis, N. (2010). Are health and happiness the product of wisdom? The relationship of general mental ability to educational and occupational attainment, health, and well-being. Journal of Applied Psychology, 95(3), 454–468. http://dx.doi.org/10.1037/a0019084.
631
Kahneman, D., Diener, E., & Schwarz, N. (Eds.). (1999). Well-being: The foundations of hedonic psychology. New York, NY: Russell Sage Foundation. Krohne, H. W., Egloff, B., Kohlmann, C. W., & Tausch, A. (1996). Untersuchungen mit einer deutschen version der Positive and Negative Affect Schedule (PANAS). [Studies with a German version of the Positive and Negative Affect Schedule (PANAS)]. Diagnostica, 42, 139–156. Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2004). Academic performance, career potential, creativity, and job performance. Can one construct predict them all? Journal of Personality and Social Psychology, 86(1), 148–161. http://dx.doi.org/ 10.1037/0022-3514.86.1.148. Little, T. D., Cunningham, W. A., Shahar, G., & Widaman, K. F. (2002). To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 151–173. http://dx.doi.org/10.1207/ S15328007SEM0902_1. Muthén, L. K., & Muthén, B. O. (1998). Mplus user’s guide (5th ed.). Los Angeles, CA: Muthén & Muthén. Pavot, W., & Diener, E. (1993). Review of the satisfaction with life scale. Psychological Assessment, 5(2), 164–172. http://dx.doi.org/10.1037/1040-3590.5.2.164. Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A., & Goldberg, L. R. (2007). The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspective on Psychological Science, 2(4), 313–345. http://dx.doi.org/10.1111/j.17456916.2007.00047.x. Rosenberg, M. (1965). Society and the adolescent self-image. Princeton, NJ: Princeton University Press. Strenze, T. (2007). Intelligence and socioeconomic success: A meta-analytic review of longitudinal research. Intelligence, 35(5), 401–426. http://dx.doi.org/10.1016/ j.intell.2006.09.004. UNESCO (1997). International Standard Classification of Education. UNESCO Press.. Veenhoven, R., & Choi, Y. (2012). Does intelligence boost happiness? Smartness of all pays more that being smarter than others. International Journal of Happiness and Development, in press. Veenhoven, R. (2009). How do we assess how happy we are? In A. K. Dutt & B. Radcliff (Eds.), Happiness, economics and politics: Towards a multi-disciplinary approach (pp. 45–69). Cheltenham, UK: Edward Elger Publishers. Wilkinson, L. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54(8), 594–604. http://dx.doi.org/10.1037/ 0003-066X.54.8.594. Wrulich, M., Brunner, M., Stadler, G., Schalke, D., Keller, U., & Martin, R. (in press). Brief Report. Forty years on: Childhood intelligence predicts health in middle adulthood. Health Psychology.
Contents lists available at SciVerse ScienceDirect
Journal of Research in Personality journal homepage: www.elsevier.com/locate/jrp
Brief Report
Does childhood general cognitive ability at age 12 predict subjective well-being at age 52? Magda Chmiel a,⇑, Martin Brunner b,1, Ulrich Keller a, Daniela Schalke a, Marius Wrulich a, Romain Martin a a b
Center for Educational Measurement and Applied Cognitive Science (EMACS), University of Luxembourg, L-7220 Walferdange, Luxembourg Freie Universität Berlin Kaiserswerther Straße 16/18 14195 Berlin, Germany
a r t i c l e
i n f o
Article history: Available online 28 June 2012 Keywords: Subjective well-being General cognitive ability Life satisfaction Positive affect Negative affect Domain satisfaction with life Socioeconomic status
a b s t r a c t Drawing on a broad, multidimensional conceptualization of subjective well-being, this study examined the power of childhood general cognitive ability to predict life satisfaction, satisfaction with eight individual life domains, and the frequency of experiencing positive and negative affect in middle adulthood. Data were obtained from a representative Luxembourgish sample (N = 738; 53% female) in a longitudinal study conducted in 1968 and 2008. Childhood general cognitive ability was unrelated to life satisfaction, negatively related to negative affect and satisfaction with free time, and positively related to positive affect and satisfaction with some of the life domains associated with socioeconomic success (i.e. finances, self, housing, work, or health). This predictive power persisted even when childhood socioeconomic status was controlled. Ó 2012 Elsevier Inc. All rights reserved.
1. Introduction Subjective well-being (SWB) is a key life outcome (Diener, 1984). One comprehensive and widely applied conceptualization of SWB, developed by Diener, Suh, Lucas, and Smith (1999), distinguishes four major components of SWB (see e.g. Veenhoven (2009) for some alternative conceptualizations of SWB): (a) Life satisfaction (LS) is a cognitive evaluation of the quality of one’s life as a whole; that is, satisfaction with one’s current life circumstances, one’s past, or one’s outlook on the future. (b) Positive affect (PA) refers to recent occurrences of positive emotions such as joy, contentment, or pride. (c) Negative affect (NA) refers to recent occurrences of unpleasant emotions such as guilt, shame, or sadness. (d) Domain satisfaction refers to the level of contentment with specific domains of one’s life such as work, family, people, free time, health, finances, housing, and the self (the last item is also referred to as self-esteem). The major objective of the present paper is to explore the relation of SWB components to general cognitive ability (GCA). GCA (as measured by intelligence tests) has been repeatedly shown to predict key outcome variables such as education, occupation, finance (socioeconomic status; SES), and health (Gottfredson & Deary,
⇑ Corresponding author. Address: University of Luxembourg, Center for Educational Measurement and Applied Cognitive Science (EMACS), Campus Walferdange, L-7220 Walferdange, Luxembourg. E-mail address: [email protected] (M. Chmiel). 1 The Berlin-Brandenburg Institute for School Quality Improvement (ISQ) Otto-vonSimson-Str. 15 14195 Berlin, Germany. 0092-6566/$ - see front matter Ó 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jrp.2012.06.006
2004; Kuncel, Hezlett, & Ones, 2004; Wrulich et al., in press). Moreover, SES has been found to be positively related to various components of SWB (Howell & Howell, 2008). Thus, given that GCA is related to indicators of SES (i.e. education, finance, and occupation) or variables that are also related to SES (i.e. health), we expected that childhood GCA would be related to the level of SWB components that are related to achieving success in life. Previous research on the relation between GCA and SWB has yielded mixed results. For example, three comprehensive metaanalytic studies that examined the relations between GCA and SWB (Ackerman & Heggestad, 1997; DeNeve & Cooper, 1998; Veenhoven & Choi, 2012) found average correlations between GCA and SWB of around zero. However, although each meta-analysis provided valuable scientific insights, we believe that it may be premature to conclude from these results that the true relations between GCA and all components of SWB are actually zero: (a) Most previous research in this area drew on convenience samples. Hence, these samples very likely captured only a restricted range of the variability of GCA and/or the various components of SWB in the target populations. This in turn may have led to an underestimation of the true relations between GCA and the components of SWB. (b) The previous meta-analyses did not analyze the impact of GCA on individual SWB components separately but rather averaged the results obtained for correlated but distinct components of SWB. Hence, positive and negative correlations between GCA and SWB components may have cancelled each other out, contributing to the finding that the correlations averaged to zero. The major goal of the present paper is to explore the long-term effects of childhood GCA on key components of SWB over a 40-year
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time period. To overcome some of the methodological constraints of previous research, we capitalized on a research design that (a) applied the multidimensional conceptualization of SWB by Diener et al. (1999), (b) drew on a representative adult sample, and (c) controlled for the potentially confounding impact of childhood SES on the SWB components in middle adulthood (see Kahneman, Diener, & Schwarz, 1999). 2. Method 2.1. Sample The data were obtained from the longitudinal MAGRIP project, involving two waves of measurement. In 1968, detailed data on GCA and family background were collected from about half the Luxembourgish student population at the end of primary school when most respondents were in Grade 6 (N = 2,824; M = 11.9 years, SD = 7.2 months; 50.1% male; 84.1% Luxembourgish origin). The second wave of measurement was conducted between November 2008 and February 2009. For budgetary reasons, it was not possible to contact all of the former students for whom addresses were available in 2008 (i.e. 2377 out of 2,824 participants). Thus, a random stratified representative sample of 1,632 persons was drawn from the available addresses. Stratification criteria were (a) place of residence in 1968 and (b) gender. Out of these 1,632 persons, 745 persons took part in the second wave of measurement, 300 were unable to contact, and 587 refused to participate. Out of the 745 participants, 738 (M = 51.8 years; SD = 6.6 months; 47.0% male; 85% Luxembourgish origin) completed a comprehensive questionnaire on SWB. They did not receive any remuneration. Analyses for selection bias showed that the follow-up sample of 738 persons was fairly representative of the original sample, with slightly higher mean childhood GCA (Cohen’s d = 0.20) and childhood SES (d = 0.08). 2.2. Measures The various components of SWB were assessed by valid and reliable measures. (The online supplement contains a complete list of items (Table 1) and a correlation of all SWB components that were represented as latent variables (Table 3)). Specifically, life satisfaction was measured by the widely used Satisfaction with Life Scale (Diener, Emmons, Larsen, & Griffin, 1985; Cronbach’s a = .84). Satisfaction with the domain self (a = .62) was assessed
using four items from the Rosenberg (1965) self-esteem scale. Satisfaction with the remaining seven domains was measured by four items taken from the Fragebogen zur Lebenszufriedenheit (Fahrenberg, Myrtek, Schumacher, & Brähler, 2000), respectively. The reliabilities of the domain satisfaction scale scores ranged from a = .62 for people to a = .85 for work, finances, and free time (see also Table 1 in the online supplemental materials). The frequency of experiencing positive affect (PA) and negative affect (NA) was measured by the German version of the Positive Affect Negative Affect Schedule (PANAS; Krohne, Egloff, Kohlmann, & Tausch, 1996). Participants stated how often they generally experienced a certain emotion. The reliability of each of the scale scores was a = .80. To analyze the relations of GCA to the components of SWB, each individual component was captured by corresponding parcel scores (i.e. a sum score of subsets of items from individual scales; see Little, Cunningham, Shahar, & Widaman, 2002). 2.2.1. General cognitive ability General cognitive ability (GCA) was captured as a latent factor that was measured by four scales from the Leistungsprüfsystem (L-P-S [Performance Test System]; Horn, 1962). The L-P-S is a standardized, objective, comprehensive German intelligence test. The scales are: verbal ability (3 subtests; a = .79), reasoning ability (2 subtests; a = .74), visual–spatial ability (2 subtests; a = .59), and processing speed (2 subtests; a = .36). 2.2.2. Childhood socio-economic status Childhood socio-economic status (SES) was captured as a latent factor defined by three indicators. First, the participants’ fathers’ occupations performed in 1968 (or their last performed occupations) were mapped onto the International Socio-Economic Index of Occupational Status (ISEI) scale (Ganzeboom & Treiman, 1996) with higher values indicating higher occupational prestige. Second, the respondents’ fathers’ trained occupations were again mapped onto the ISEI metric. Third, the fathers’ highest academic qualifications were mapped onto the International Standard Classification of Education (ISCED) scale (UNESCO, 1997) with higher values indicating a higher level of education. 2.2.3. Statistical analyses To explore the relations between childhood GCA and SWB in middle adulthood, we ran two different structural equation models (Fig. 1, Table 1). The standardized regression coefficients obtained for Model 1 indicates the predictive power of childhood GCA for each SWB component. Model 2 tested whether the predictive
Table 1 Prediction of SWB components by childhood general cognitive ability (Model 1) and by childhood general cognitive ability and childhood socioeconomic status (Model 2). DV
Model 1
Model 2
GCA
PA NA FI HE HO SE WO PE FA FT LS
R2
b
95% CI
.21 .11 .13 .11 .10 .09 .07 .01 –.02 –.07 .04
[.11, .31] [ .20, .02] [.05, .22] [.01, .21] [.00, .19] [ .02, .20] [ .03, .16] [ .11, .12] [ .13, .10] [ .15, .02] [ .05, .13]
GCA
.04 .01 .02 .01 .01 .01 .01 .00 .00 .00 .00
SES 95% CI
b .21 .12 .12 .10 .09 .12 .07 .02 .02 .06 .04
[.11, .31] [ .21, .02]. [.03, .21] [ .01, .20] [ .01, .19] [.00, .24] [ .02, .17] [ .10, .14] [ .10, .14] [ .15, .03] [ .06, .13]
R2 95% CI
b .01 .04 06 .03 .01 11 .02 .06 .13 .02 .02
[ [ [ [ [ [ [ [ [ [ [
.10, .07, .03, .07, .09, .24, .13, .17, .26, .11, .08,
.09] .15] .16] .13] .11] .01] .08] .05] –.01] .08] .12]
.04 .01 .02 .01 .01 .02 .00 .00 .02 .00 .00
Note. DV = components of SWB served as dependent variables in the structural equation models; GCA = general cognitive ability; SES = childhood socioeconomic status; PA = positive affect, NA = negative affect; FI = finances, HE = health, HO = housing, SE = self, WO = work; PE = people, FA = family, FT = free time, LS = life satisfaction, b = standardized regression coefficient; CI = confidence interval. The standardized regression coefficients from Model 1 are the same as correlation coefficients and can thus be interpreted as standardized effect size measures.
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(a)
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(b)
Fig. 1. Structural equation models representing (a) how childhood general cognitive ability (GCA) predicts SWB components (Model 1) and (b) whether this predictive power remains when childhood socioeconomic status (SES) is controlled for (Model 2).
power of childhood GCA persisted when a potentially confounding individual characteristic—childhood SES as measured at age 12— was controlled. Not all participants provided data for all measures: For the items that concerned all participants, data were missing for between four and 28 of 738 participants; for items that did not apply to all participants (e.g. satisfaction with one’s partner), data were missing for between 46 and 202 of 738 participants). To account for this pattern of missing data, we used the full information maximum likelihood estimation method ‘‘MLR’’ as implemented in Mplus 5.2 for all analyses (Muthén & Muthén, 1998–2006). We used the Comparative Fit Index (CFI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR) to assess the fits of Models 1 and 2.
are presented in Table 1. In Model l, childhood GCA was positively related to the frequency of experiencing PA but negatively related to the frequency of experiencing NA in middle adulthood. It also showed small positive associations with satisfaction with finances, health, housing, self, and work 40 years later. Childhood GCA did not predict the level of satisfaction with family or social life (people); however, it was negatively related to satisfaction with free time. The relation of GCA to LS in middle adulthood was negligible. Crucially, the results obtained for Model 2 demonstrate that the predictive power of GCA ability for each component of SWB persisted even when we controlled for childhood SES (see Table 1).
4. Discussion 3. Results The fits of both models were very good (see Fig. 1), with CFI values larger than .95, and RMSEA and SRMR values smaller than .04 (Hu & Bentler, 1999). Moreover, all latent factors were well defined in both models (see Table 3 in the online supplemental materials). Hence, both models allowed us to reliably address our research question. The results of our analyses regarding the relation between childhood GCA and SWB components in middle adulthood
The major goal of the present paper was to explore the longterm impact of childhood GCA on key components of SWB over a 40-year time period. Our results clearly demonstrate that the conclusion that GCA is not related to SWB needs to be reconsidered. Specifically, we found that people with higher childhood GCA were more satisfied with many domains associated with socioeconomic success in life. Persons with a higher level of childhood GCA also experienced more PA and less NA. Effect sizes for the relations between childhood GCA and various components of SWB ranged
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from .10 to .20 (see results obtained for Model 1 in Table 1). At first glance, these relations may seem small. However, they span a period of 40 years, and can be considered typical in lifespan psychology in which effect sizes tend to range between .10 and .30 (Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007). In sum, the predictive power of childhood GCA to forecast several key components of SWB over a 40-year period can be considered substantial. How can the relations between childhood GCA and SWB be explained? As previous research has shown GCA to be positively related to SES (e.g. Judge, Ilies, & Dimotakis, 2010; Strenze, 2007), it seems plausible that it is their higher SES that leads people with higher childhood GCA to experience higher satisfaction with their finances, housing, and work, as well as with themselves (self; e.g. Kuncel et al., 2004). We also suppose that achieving a higher SES can be one of the reasons for why childhood GCA was positively associated with the frequency of experiencing PA and negatively associated with the frequency of experiencing NA. As people with higher GCA are also more successful in many walks of life (Judge et al., 2010; Kuncel et al., 2004), it is very probable that they face more situations that lead them to experience more PA. Analogically, as they lead more fulfilling and engaged lives and face fewer everyday challenges than their counterparts with lower GCA, they are also less likely to experience negative emotions when faced with challenges. Moreover, they have greater cognitive and financial resources to effectively limit the impact of these challenges. A higher SES is also known to be associated with better health (Judge et al., 2010). Furthermore, as a positive impact of GCA has been demonstrated on both objective and perceived physical and mental health status via better coping mechanisms and better health behavior (Gottfredson & Deary, 2004), it may also result in a better subjective evaluation of one’s health status. Finally, the lack of association between childhood GCA and satisfaction with family and people is also aligned with our proposition that childhood GCA shows stronger associations with satisfaction with life domains that are associated with achieving socioeconomic success in life. We observed a negative association between childhood GCA and satisfaction with free time. Free time has a quantitative (i.e. amount of leisure time) and a qualitative component (i.e. quality of leisure time activities). Although higher SES might be associated with a higher quality of leisure activities, higher SES is typically also associated with more demanding jobs resulting in less leisure time. Thus, the observed negative correlation between GCA and satisfaction with free time might reflect the net effect of evaluating both components. Finally, in line with the findings of previous research (Ackerman & Heggestad, 1997; DeNeve & Cooper, 1998; Veenhoven & Choi, 2012, in our study, childhood GCA was not significantly associated with LS 40 years later. LS is the evaluation of the quality of one’s life as a whole (Diener et al., 1999). A person’s LS level is considered to be the sum of the various facets of SWB (Pavot & Diener, 1993) so that LS represents an amalgam of all SWB components (Chmiel, Brunner, Martin, & Schalke, 2012). Thus, the impact of childhood GCA on LS depends on its impact on each of the individual SWB components. However, these relations can have various vectors and strengths, which may counterbalance one another, leading to a nonsignificant overall relation between childhood GCA and LS.
ated the relations observed between childhood GCA and those SWB components. Given that we examined a representative sample of middle-aged adults and administered measures that have been widely used in previous research on SWB, this is an important finding. Future research (particularly with adult respondents) should therefore apply measures (e.g. ambulatory assessment of moods; see Kahneman et al., 1999) with a higher ceiling in order to capture the variance of key components of SWB more fully. Second, although our measures of satisfaction with various life domains were very detailed, they did not allow us to measure the individual impact of GCA on satisfaction with different aspects within certain domains. For example, satisfaction with work encompasses satisfaction with several facets (e.g. prestige, wage, stress, working hours). Such subfacets may be differently influenced by GCA (see Ganzach & Fried, 2012), which in turn may potentially lead to nonsignificant results when only the global component (e.g. satisfaction with work) is considered in the analyses. In order to detect all such possible influences, future research needs to apply more detailed measures, analyzing various facets within life domains. Third, we presented our results in terms of effect sizes and corresponding confidence intervals. In so doing, we followed the guidelines of the American Psychological Association (Wilkinson, 1999). Although many of the effects observed in the present study can be considered substantial (taking into account the 40-year time span and the potential ceiling effects), the 95% confidence intervals for some of these relations included zero. Hence, within these limits of confidence, we cannot completely rule out the possibility that some of these effects were actually zero. Thus, there is a need for further research that not only draws on representative samples and a comprehensive conceptualization of SWB, but that also uses better measures and/or larger samples in order to yield more accurate effect size estimates.
6. Conclusions Our study analyzed the long-term relations of childhood GCA to SWB from a lifespan perspective in a representative adult sample. Our findings showed that people with higher childhood GCA are more satisfied with many SWB components in middle adulthood (health, finances, housing, self), and that they more frequently experience PA and less frequently experience NA in middle adulthood. Moreover, the study demonstrated the importance of using a multidimensional conceptualization of SWB in research. We hope that future research draws on the present results and the applied methodological approach to examine the differential relations between childhood GCA and individual SWB components.
Acknowledgments This study was supported by a grant from the Luxembourgish Fonds National de la Recherché (VIVRE: Living tomorrow in Luxembourg, Project No. FNR/06/09/18) and a PhD scholarship awarded to the first author by the Fonds National de la Recherché. The authors would also like to thank Boris Egloff for providing the German version of the Positive Affect Negative Affect Schedule measures.
5. Limitations One limitation of our study relates to the measures of SWB applied. First, we used self-report measures, which showed a ceiling effect for most of the SWB components examined (see Figs. 1–11 in the supplementary online material). This effect may have attenu-
Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.jrp.2012.06.006.
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References Ackerman, P. L., & Heggestad, E. D. (1997). Intelligence, personality, and interests: Evidence for overlapping traits. Psychological Bulletin, 121(2), 219–245. http:// dx.doi.org/10.1037/0033-2909.121.2.219. Chmiel, M., Brunner, M., Martin, R., & Schalke, D. (2012). Revisiting the structure of subjective well-being in middle-aged adults. Social Indicators Research, 106(1), 109–116. http://dx.doi.org/10.1007/s11205-011-9796-7. DeNeve, K. M., & Cooper, H. (1998). The happy personality: A meta-analysis of 137 personality traits and subjective well-being. Psychological Bulletin, 124(2), 197–229. http://dx.doi.org/10.1037//0033-2909.124.2.197. Diener, E. (1984). Subjective well-being. Psychological Bulletin, 95(3), 542–575. http://dx.doi.org/10.1037/0033-2909.95.3.542. Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satisfaction with life scale. Journal of Personality Assesment, 49(1), 71–75. http://dx.doi.org/10.1207/ s15327752jpa4901_13. Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-being: Three decades of progress. Psychological Bulletin, 125(2), 276–302. http://dx.doi.org/ 10.1037/0033-2909.125.2.276. Fahrenberg, J., Myrtek, M., Schumacher, J., & Brähler, E. (2000). Fragebogen zur Lebenszufriedenheit. Handanweisung. Göttingen, Germany: Hogrefe. Satisfaction with life questionnaire. Manual. Ganzach, Y., & Fried, I. (2012). The role of intelligence in the formation of wellbeing: From job rewards to job satisfaction. Intelligence, 40(4), 333–342. http:// dx.doi.org/10.1016/j.intell.2012.03.004. Ganzeboom, H. B. G., & Treiman, D. J. (1996). Internationally comparable measures of occupational status for the 1988 International Standards Classification of Occupations. Social Science Research, 25(3), 201–239. http://dx.doi.org/10.1006/ ssre.1996.0010. Gottfredson, L. S., & Deary, I. J. (2004). Intelligence predicts health and longevity, but why? Current Directions in Psychological Science, 13(1), 1–4. http://dx.doi.org/ 10.1111/j.0963-7214.2004.01301001.x. Horn, W. (1962). Leistungsprüfsystem L-P-S. Göttingen, Germany: Hogrefe. Performance Test System L-P-S. Howell, R. T., & Howell, C. J. (2008). The relation of economic status to subjective well-being in developing countries: A meta-analysis. Psychological Bulletin, 134(4), 536–560. http://dx.doi.org/10.1037/0033-2909.134.4.536. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. http://dx.doi.org/10.1080/ 10705519909540118. Judge, T. A., Ilies, R., & Dimotakis, N. (2010). Are health and happiness the product of wisdom? The relationship of general mental ability to educational and occupational attainment, health, and well-being. Journal of Applied Psychology, 95(3), 454–468. http://dx.doi.org/10.1037/a0019084.
631
Kahneman, D., Diener, E., & Schwarz, N. (Eds.). (1999). Well-being: The foundations of hedonic psychology. New York, NY: Russell Sage Foundation. Krohne, H. W., Egloff, B., Kohlmann, C. W., & Tausch, A. (1996). Untersuchungen mit einer deutschen version der Positive and Negative Affect Schedule (PANAS). [Studies with a German version of the Positive and Negative Affect Schedule (PANAS)]. Diagnostica, 42, 139–156. Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2004). Academic performance, career potential, creativity, and job performance. Can one construct predict them all? Journal of Personality and Social Psychology, 86(1), 148–161. http://dx.doi.org/ 10.1037/0022-3514.86.1.148. Little, T. D., Cunningham, W. A., Shahar, G., & Widaman, K. F. (2002). To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 151–173. http://dx.doi.org/10.1207/ S15328007SEM0902_1. Muthén, L. K., & Muthén, B. O. (1998). Mplus user’s guide (5th ed.). Los Angeles, CA: Muthén & Muthén. Pavot, W., & Diener, E. (1993). Review of the satisfaction with life scale. Psychological Assessment, 5(2), 164–172. http://dx.doi.org/10.1037/1040-3590.5.2.164. Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A., & Goldberg, L. R. (2007). The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspective on Psychological Science, 2(4), 313–345. http://dx.doi.org/10.1111/j.17456916.2007.00047.x. Rosenberg, M. (1965). Society and the adolescent self-image. Princeton, NJ: Princeton University Press. Strenze, T. (2007). Intelligence and socioeconomic success: A meta-analytic review of longitudinal research. Intelligence, 35(5), 401–426. http://dx.doi.org/10.1016/ j.intell.2006.09.004. UNESCO (1997). International Standard Classification of Education. UNESCO Press.