Psychology

PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES

Can Personality Traits and Intelligence Compensate for Background Disadvantage? Predicting Status Attainment in Adulthood

Rodica Ioana Damian University of Illinois at Urbana-Champaign

Rong Su Purdue University

Michael Shanahan University of North Carolina at Chapel Hill

Ulrich Trautwein University of Tübingen

Brent W. Roberts University of Illinois at Urbana-Champaign

This study investigated the interplay of family background and individual differences, such as personality traits and intelligence (measured in a large U.S. representative sample of high school students; N � 81,000) in predicting educational attainment, annual income, and occupational prestige 11 years later. Specifically, we tested whether individual differences followed 1 of 3 patterns in relation to parental socioeconomic status (SES) when predicting attained status: (a) the independent effects hypothesis (i.e., individual differences predict attainments independent of parental SES level), (b) the resource substitution hypothesis (i.e., individual differences are stronger predictors of attainments at lower levels of parental SES), and (c) the Matthew effect hypothesis (i.e., “the rich get richer”; individual differences are stronger predictors of attainments at higher levels of parental SES). We found that personality traits and intelligence in adolescence predicted later attained status above and beyond parental SES. A standard deviation increase in individual differences translated to up to 8 additional months of education, $4,233 annually, and more prestigious occupations. Furthermore, although we did find some evidence for both the resource substitution and the Matthew effect hypotheses, the most robust pattern across all models supported the independent effects hypothesis. Intelligence was the exception, the interaction models being more robust. Finally, we found that although personality traits may help compensate for background disadvantage to a small extent, they do not usually lead to a “full catch-up” effect, unlike intelligence. This was the first longitudinal study of status attainment to test interactive models of individual differences and background factors.

Keywords: personality, education, occupational prestige, socioeconomic status, resource substitution

A renaissance in personality psychology has emerged both within psychology and in related social sciences, such as economics and

educational research, because of hard evidence that “soft” skills (i.e., personality traits) predict important status attainment outcomes, such as educational and occupational success (Heckman & Kautz, 2012). Traditionally, social sciences have focused on the merits of using presumably “tougher” skills (i.e., cognitive abilities) and background factors (i.e., socioeconomic status [SES]) as predictors of status at- tainment. Recent research, however, has shown that, when predicting status attainment, personality characteristics provide added value be- yond the more widely accepted predictors of cognitive ability and socioeconomic background (Heckman, 2006; Moffitt et al., 2011; Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007). And, with great regularity, personality traits such as conscientiousness have shown incremental predictive power for outcomes such as educational achievement (Poropat, 2009).

Seldom considered, but always a possibility, is the fact that soft skills such as personality traits may have more complex relation- ships with the variables typically used to predict educational and occupational attainment. Specifically, rather than thinking simply in terms of whether a trait such as conscientiousness adds valuable information above and beyond family background, it is possible to

This article was published Online First November 17, 2014. Rodica Ioana Damian, Department of Psychology, University of Illinois at

Urbana-Champaign; Rong Su, Department of Psychological Sciences, Purdue University; Michael Shanahan, Department of Sociology, University of North Carolina at Chapel Hill; Ulrich Trautwein, Hector Research Institute of Edu- cation Sciences and Psychology, University of Tübingen; Brent W. Roberts, Department of Psychology, University of Illinois at Urbana-Champaign.

This research uses data from Project Talent, a project directed by the American Institutes of Research (AIR). Information on how to obtain the Project Talent data files is available on the AIR website (http://www.air.org/) and from Susan Lapham ([email protected]). This research was supported by a grant from National Institute on Aging (R01 AG21178, Brent W. Roberts, Principal Investigator). We thank Professor Oliver Lüdtke for comments on a draft of the manuscript.

Correspondence concerning this article should be addressed to Rodica Ioana Damian, Department of Psychology, University of Illinois at Urbana-Champaign, 603 E. Daniel Street, Champaign, IL 61820. E-mail: [email protected]

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Journal of Personality and Social Psychology, 2015, Vol. 109, No. 3, 473– 489 © 2014 American Psychological Association 0022-3514/15/$12.00 http://dx.doi.org/10.1037/pspp0000024

473

consider more complex, multiplicative combinations of family background with personality. That is to say, individuals with certain characteristics may do disproportionately better or worse depending on whether they were born into a more challenging or privileged family context.

This study investigated the interplay of family background and the effects of individual differences, such as personality traits and intelligence (measured in a large U.S. representative sample of high school students), in predicting educational attainment, annual income, and occupational prestige 11 years later. Specifically, we tested whether individual differences in personality traits and intelligence follow one of three patterns in relation to parental SES when predicting attained status: (a) the independent effects hy- pothesis (i.e., personality characteristics and intelligence predict attainments independent of parental SES level), (b) the resource substitution hypothesis (i.e., personality characteristics and intel- ligence are stronger predictors of attainments at lower levels of parental SES), and (c) the Matthew effect hypothesis (i.e., “the rich get richer”; personality characteristics and intelligence are stronger predictors of attainments at higher levels of parental SES).1

The present study is the first to test these three hypotheses and the possible moderating role of parental SES on the prospective effects of personality traits and intelligence on status attainment. Moreover, in doing so, we used a longitudinal design and one of the largest nationally representative samples in the world, that is, the Project Talent data.

The Interplay of Parental SES With Personality Traits and Intelligence in Predicting

Status Attainment

Investigations of educational and occupational status attainment, being driven by distinctly different intellectual guilds, have tended to avoid integrative work. The psychological literature on status attainment tends to focus on personality trait and cognitive ability predictors and correlates (e.g., Duckworth, Weir, Tsukayama, & Kwok, 2012; Roberts, Caspi, & Moffitt, 2003). In contrast, soci- ological literature on status attainment tends to focus on parental social class predictors and correlates (Child, 1969). Like person- ality traits and cognitive abilities, parental SES has been found to affect children’s later educational attainment, income, and occu- pational prestige (G. J. Duncan & Brooks-Gunn, 1997; O. D. Duncan, Featherman, & Duncan, 1972; Johnson, McGue, & Iacono, 2007). Although these two lines of research have devel- oped in parallel, there is very little research, to date, bridging the two traditions. Thus, studying the interactive effects of individual difference factors, such as personality traits and intelligence, with environmental factors, such as parental SES, is the next logical step in improving our models of status attainment.

The default model, which is implicitly reflected in the research siloed in the respective psychological or sociological literatures, is the independent effects model. The independent effects model presup- poses no interaction between parental SES and either personality traits or intelligence. We refer to this as the default model because it has been the standard approach taken by researchers desiring to demon- strate the incremental validity of predictors such as conscientiousness on outcomes such as educational attainment. Ample research has shown that both personality traits and cognitive abilities have unique contributions (above and beyond each other) on attainment outcomes.

For example, personality traits such as conscientiousness predict grade point average both in high school (Poropat, 2009) and college (Noftle & Robins, 2007). Moreover, conscientiousness predicts dif- ferences in income levels (Roberts, Jackson, Duckworth, & Von Culin, 2011) and, in some cases, net of parental SES and cognitive ability (Moffitt et al., 2011). Likewise, cognitive ability is also a strong predictor of educational attainment and academic outcomes (Cawley, Heckman, & Vytlacil, 2001; Gottfredson, 2002; Gustafsson & Undheim, 1996; Kuncel, Hezlett, & Ones, 2004). Most of this prior research has not reported tests of interaction between parental SES and either personality traits or cognitive ability, but we take the univariate incremental effects of both as evidence for the independent effects model. To the extent that this is the only pattern to be found, we would expect personality traits and intelligence to have the same effect on status attainment for all people across parental SES levels.

Of course, the relation between personality factors, parental SES, and attainment may not be so straightforward. For example, Johnson, McGue, and Iacono (2006), showed that intelligence mitigated the negative effects of disadvantaged backgrounds on school achieve- ment. Furthermore, according to Shanahan, Bauldry, Roberts, Mac- millan, and Russo (2014), personality traits and intelligence may be more strongly associated with attained status at lower levels of parent education. In other words, personality traits and intelligence (mea- sured in high school) may compensate for background disadvantage with respect to educational attainment, income, and occupational prestige 11 years later. If so, the returns to specific personality traits and intelligence would be greater for people overcoming limited socioeconomic resources of their parents. In line with this reasoning, Mirowsky and Ross (2003) proposed the “resource substitution hy- pothesis,” which states that resources will have more beneficial effects among people with fewer alternative resources. For example, personal education influences health more so among people with less educated parents (Ross & Mirowsky, 2011). Resource substitution implies a moderating pattern such that one resource (personal education) be- comes increasingly salient at lower levels of another critical resource (parental education). Such a pattern has been found with respect to the effect of a college degree on future earnings (Brand & Xie, 2010).

Although previously formulated with reference to education and health, resource substitution may well apply to personality traits, intelligence, and status attainment. Some of the compensatory effects of personal education are likely related to conscientious behaviors. Indeed, Mirowsky and Ross (2003) suggest that personal education is compensatory because—in addition to cognitive skills (such as ana- lytic reasoning) and self-efficacy—attained education reflects the capacity to develop plans and implement them by way of conscien- tious behaviors such as planning, engagement in goal-directed behav- iors, and perseverance. The interventions reviewed by Heckman and Kautz (2012) also suggested the importance of agreeableness and emotional stability in school settings. That is, some of the substitution effect of child’s education that was observed by Mirowsky and Ross (2003) is likely attributable to personality characteristics that promote both education and health (see also, Hauser & Palloni, 2011). More- over, children from lower-class households may be lacking diverse forms of cultural capital (linguistic and behavioral patterns indicative

1 Other fields, such as sociology or educational research, may have different definitions of the “Matthew effect.” However, for the present article, we limit our definition of this effect to the one provided in the text.

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474 DAMIAN, SU, SHANAHAN, TRAUTWEIN, AND ROBERTS

of middle-class upbringing; Bourdieu & Passeron, 1977), but such children may compensate for these missing resources by being con- scientiousness, agreeable, emotionally stable, and open.

Another possibility besides resource substitution is the so called “Matthew effect” or “the rich get richer” effect. According to this hypothesis, children raised in higher SES households will benefit more from certain personality traits and intelligence, presumably because their environment facilitates and enhances the positive effects of specific traits (e.g., a child with a high verbal ability who finds herself in a higher SES household will have access to more books, which will further increase her chances for high status attainment). Indeed, a previous cross-sectional study (Walberg & Tsai, 1983) found that young adults who were higher in parental SES benefited more from a better educational background and from more school motivation. Similar evidence for the Matthew effect can be found across many areas of inquiry. For example, Merton (1968) showed that highly ranked scientists received dis- proportionately higher praise from subsequent achievements, com- pared with lower ranked scientists. Another area with extensive evidence for the Matthew effect is memory research, in which the effects of memory training appear to be enhanced for those who are younger and begin training with more cognitive resources (Bissig & Lustig, 2007; Verhaeghen & Marcoen, 1996; Verhae- ghen, Marcoen, & Goossens, 1992).

Despite the theoretical and practical importance of testing these hypotheses in the context of status attainment, we know of only one article to date that has attempted to do so. Specifically, the article by Shanahan and colleagues (2014) investigated the role of personality traits on educational attainment and wages, as moder- ated by parental education. Regarding educational outcome, the authors found evidence for resource substitution for three of the Big Five personality traits, namely, Emotional Stability (the op- posite end of neuroticism), Agreeableness, and Openness. Regard- ing wages, the authors replicated the finding that Emotional Sta- bility shows evidence of resource substitution.

Although these findings further our understanding of individual difference and environmental factors interacting in the context of status attainment, it is important to note that this study did not include intelligence as a predictor, or occupational prestige as an outcome, and did not include a moderator of parental SES (just parental education). Finally, perhaps the most important drawback of this study is that personality traits and the status outcomes were measured concurrently, not prospectively.

Present Study Overview

As mentioned earlier, this article investigates the prospective effects of personality traits and intelligence in predicting educa- tional attainment, annual income, and occupational prestige 11 years later, as well as the way parental SES moderates these links. Specifically, we test three hypotheses: (a) the independent effects hypothesis (i.e., personality characteristics and intelligence predict attainments independent of parental SES level), (b) the resource substitution hypothesis (i.e., personality characteristics and intel- ligence are stronger predictors of attainments at lower levels of parental SES), and (c) the Matthew effect hypothesis (i.e., “the rich get richer”; personality characteristics and intelligence are stronger predictors of attainments at higher levels of parental SES).

The present article improves upon previous research in several significant ways: (a) it is the first article to test the resource substitution hypothesis in a longitudinal setting (the previous ar- ticle by Shanahan and colleagues, 2014, measured personality traits concurrent with the outcomes, whereas we measured teenage personality and adult outcomes 11 years later); (b) we added intelligence to the list of predictors, and we tested the robustness of our personality trait results by controlling for intelligence, and the robustness of our intelligence results by controlling for the personality traits; (c) we added occupational prestige as an out- come, in addition to education and annual income; (d) we used one of the largest nationally representative samples currently available, namely, the Project Talent data, and we used weighted regressions to ensure that our sample was still representative of the population upon attrition; (e) we used moderated polynomial regressions to test our hypotheses and response surface methodology to visualize the results in a three dimensional space (Edwards, 2002; Shanock, Baran, Gentry, Pattison, & Heggestad, 2010); and (f) we used meaningful raw metrics to interpret effect sizes.

Method

Participants

The data come from Project Talent (see Flanagan et al., 1960; Wise, McLaughlin, & Steel, 1979), a national longitudinal study developed by the American Institutes for Research. The original survey was conducted in 1960 on a 5% representative sample of U.S. high school students. Over 440,000 students in Grades 9 through 12 participated, out of which about 377,000 cases are now available. After the original testing, the participants were recontacted via mail three times, at 1st, 5th, and 11th years after their high school gradu- ation. The response rates to the follow-up studies were 51.4% for the 1-year follow-up, 35.3% for the 5-year follow-up, and 25.8% for the 11-year follow-up. In the present study, we were interested in the role of personality traits, cognitive abilities, and parental SES on later success, specifically, educational attainment, annual income, and oc- cupational prestige. Thus, to ensure that the participants had as much time as possible to complete their education and get jobs, we used participants’ responses from the original study and the third follow-up survey (i.e., 11th year after their high school graduation).

Project Talent is the only nationally representative longitudinal study in the United States of such large scale. It is an ideal data set for studying how personality traits and cognitive abilities, along with parental SES, impact status attainment later in life. The longitudinal design of Project Talent has clear advantages over cross-sectional studies, as it allows for studying the effects of individual differences at an early age on outcomes at later life stages (Tharenou, 1997).

For the first set of analyses, we used the full available sample from the original survey (Time 1) to investigate the links between parental SES, personality traits, and cognitive ability. For the second set of analyses, we used the reduced sample of approxi- mately 81,000 participants who reported their educational attain- ment 11 years later, annual income, and job title from which we derived a measure of occupational prestige (Time 2). We con- ducted an attrition analysis to see whether the people who dropped out from the study differed from those who stayed in the study in terms of parental SES, personality traits, and cognitive ability.

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475PERSONALITY, SOCIAL BACKGROUND, AND ATTAINMENT

Measures

The original survey (Time 1) recorded the students’ personality traits, cognitive abilities, parental SES, and demographics (gender, race, and high school cohort). The 11-year follow-up (Time 2) recorded the students’ educational attainment, annual income, and occupational prestige. Next we describe each of the measures used in the present study, the original coding procedures, as well as transformations we performed.

Personality traits. The Project Talent Personality Inventory (PTPI) included 150 items from which 10 different scale compos- ites were scored and recorded. The Vigor scale measures the physical activity level of a person. The Calmness scale measures the ability to react to emotional situations in an appropriate manner without extreme emotions. The Mature Personality scale measures the ability to get work done efficiently and to accept assigned responsibility. The Impulsiveness scale measures the tendency to make quick decisions without full consideration of the outcomes. The Self-Confidence scale measures one’s feelings of social ac- ceptability and the willingness to act and think independently. The Culture scale measures the tendency to recognize the value of aesthetic things, and to display refinement and good taste. The Sociability scale measures the tendency to enjoy being with peo- ple. The Leadership scale measures activities such as taking charge and seeking out responsibilities. The Social Sensitivity scale mea- sures the propensity to put oneself in another’s place. Finally, the Tidiness scale measures the desire for order and neatness in one’s environment. For each item, participants rated how well the item described them on a 5-point scale (extremely well to not very well). Item-level data are, unfortunately, not available to researchers today for the entire sample (only for 4% of the sample), which is why we relied on the scale scores computed by the Project Talent staff.

In previous work on independent participant samples of a sim- ilar age (Pozzebon et al., 2013), we established the validity and reliability of the 10 PTPI scales, and we identified how the 10 PTPI scales relate to modern Big Five inventories (e.g., John, Donahue, & Kentle, 1991). Thus, Emotional Stability was best captured in the PTPI by the Self-Confidence (� � .78) and

Calmness (� � .87) scales; Extraversion was best captured by the Sociability (� � .83), Vigor (� � .86), and Leadership (� � .79) scales; Openness was best captured by the Culture (� � .81) scale; Agreeableness was best captured by the Social Sensitivity (� � .85) scale; and Conscientiousness was best captured by the Mature Personality (� � .93), Impulsiveness (� � .72; reverse scored), and Tidiness (� � .86) scales (for reliabilities and construct validation, see Pozzebon et al., 2013).

To make the present research more comparable with previous research on personality traits and status attainment, we computed Big Five personality trait composites from the PTPI scales, as described earlier. We first standardized all 10 personality traits, then we computed Big Five composites by averaging the relevant scales (e.g., Extraversion was computed from the standardized Sociability, Vigor, and Leadership scales), and then we restandard- ized the resulting Big Five scales prior to the analyses (note that the last two steps were not necessary for Openness and Agreeable- ness, because these scales had only one respective counterpart among the PTPI scales). Intercorrelations among the resulting Big Five personality scales ranged from .39 between Extraversion and Conscientiousness, to .61 between Agreeableness and Openness (see Table 1).

Although we report only the findings using the Big Five per- sonality composites, we also conducted all the analyses on the individual PTPI scales. The detailed results can be found in our online supplemental materials at this address: https://osf.io/bsz8g/. Importantly, the findings and conclusions were similar when com- paring the PTPI scales with the Big Five composites.

Cognitive abilities. The Project Talent original survey con- tains a set of scales that represent different content domains of cognitive abilities, including verbal, quantitative, and visualization and spatial abilities. Following past research (e.g., Su, 2012; Wai, Lubinski, & Benbow, 2009) and the radex model of cognitive ability, which organizes ability in three subdomains—verbal, mathematical, and spatial—we developed composite measures for these three abilities. We used unit weighting in constructing the composites, so no ability scale was overweighted.

Table 1 Intercorrelations Among All Variables

No. Variable M SD n 1 2 3 4 5 6 7 8 9 10 11 12

1 Gender .52 .50 346,660 — 2 Race .05 .21 147,873 .04 — 3 Cohort 10.43 1.10 346,660 .00 �.02 — 4 Parental SES 98.07 10.09 331,961 �.02 �.18 .08 — 5 Extraversion .00 1.00 346,386 .07 �.01 .06 .19 — 6 Agreeableness .05 1.00 346,386 .24 �.01 .13 .17 .56 — 7 Conscientiousness .00 1.00 346,386 .13 �.01 .10 .11 .39 .46 — 8 Emotional Stability .00 1.00 346,386 .04 �.01 .14 .19 .55 .50 .47 — 9 Openness .04 1.00 346,386 .26 .01 .11 .18 .54 .61 .51 .48 —

10 Intelligence .00 1.00 339,114 �.15 �.23 .24 .44 .10 .12 .14 .21 .10 — 11 Educational attainment (Y11) 3.07 1.17 81,075 �.22 �.04 �.02 .42 .13 .08 .13 .16 .12 .52 — 12 Income Ln (Y11) 9.07 .65 58,474 �.49 �.04 �.08 .13 .04 �.07 �.00 .04 �.09 .18 .27 — 13 Prestige (Y11) 47.30 21.31 58,575 �.11 �.06 �.01 .35 .14 .11 .15 .16 .14 .45 .74 .30

Note. Because most of the Big Five personality traits and Intelligence were composite measures, we used standardized scores here. We used raw scores for the other variables. Gender and race were dummy coded (men � 0, women � 1; Whites � 0, non-Whites � 1). All correlations higher than .00 were significant at p � .001. Ln � natural logarithm; SES � socioeconomic status; Y11 � Year 11.

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476 DAMIAN, SU, SHANAHAN, TRAUTWEIN, AND ROBERTS

The verbal ability composite (� � .88) consists of three scales: Vocabulary, English Composite, and Reading Comprehension. The math ability composite (� � .93) consists of four scales: Mathematics Information, Arithmetic Reasoning, Introductory Mathematics, and Advanced Mathematics. The spatial ability com- posite (� � .80) consists of four scales: Two-Dimensional Spatial Visualization, Three-Dimensional Spatial Visualization, Mechan- ical Reasoning, and Abstract Reasoning. In addition to the three indices (verbal, math, and spatial), we also computed an overall intelligence index, which was obtained by averaging the stan- dardized scores of the three cognitive ability indices. The resulting intelligence index was also standardized prior to all the analyses.

Although we report only the findings using the intelligence com- posite, we also conducted all the analyses on the individual cognitive ability scales. The detailed results can be found in our online supple- mental materials at this address: https://osf.io/bsz8g/. Importantly, the findings and conclusions were similar when comparing the three cognitive ability scales with the intelligence composite.

Parental SES. Project Talent provides excellent data on SES (Wise et al., 1979). The original SES composite included answers to nine questions regarding home value, family income, number of books in the house, number of appliances, number of electronics, availability of a private room for the child, father’s job status, father’s education, and mother’s education (� � .69). These are all frequently used indicators of SES in the family of origin (Galo- bardes, Shaw, Lawlor, Lynch, & Davey Smith, 2006). The index scores ranged from 59 to 131, and were standardized prior to the analyses.

Demographic measures. Three demographic measures were included in all the analyses, because there are well documented effects of these variables on educational attainment and career success outcomes: gender, race/ethnicity, and age cohort. Gender was coded as male � 0, female � 1. Race/ethnicity was coded using a 1 to 9 scale (the labels at the time were 1 � White/ Caucasian, 2 � Black/African American, 3 � Asian American, 4 � Native American, 5 � Mexican American, 6 � Puerto Rican American, 7 � Eskimo, 8 � Cuban, 9 � Unknown). Because the numbers in each of the non-Caucasian racial categories were very small, we recoded race into a dummy variable, in which “0” was “Other” and “1” was “White/Caucasian.” For the regression anal- yses, gender and race were effects-coded (men � �1, women � 1; other � �1, White/Caucasian � 1). Cohort represents the grade (9th, 10th, 11th, or 12th) that participants were in at the original survey. It was coded as a numeric variable ranging from 9 to 12, with a larger number standing for an older cohort. Cohort was an important variable to take into account because all participants took the same tests at Time 1 even though they belonged to different age groups; thus, cognitive ability test scores for instance vary quite a bit, with the older students scoring better. Finally, all our analyses consist of weighted least squares regressions, in which each case available at Time 2 was weighted appropriately in order to produce parameter estimates closer to the representative sample collected at Time 1 (for details on how the respondent case weights were computed by the Project Talent staff, see Wise et al., 1979).

Educational attainment. At the 11th-year follow-up (Time 2), participants answered several questions regarding their educa- tional pursuits and outcomes, based on which the Project Talent staff coded an “amount of education” variable using a 12-point

scale. Thus, in the original coding, Scores 0 through 4 referred to high school dropouts (each happening in a different grade from 8 to 12, respectively); Scores 5 and 6 referred to high school grad- uates who had no post-high-school education, or some post-high- school education, but no college; Score 7 referred to high school graduates who had some college experience, but no college degree; Scores 8 and 9 referred to college graduates who had no further graduate school experience, or some graduate school experience, but no graduate degree; Scores 10 and 11 referred to master’s graduates who had no further graduate education, or some graduate education, but no further degree beyond the master’s; and finally, Score 12 referred to holders of doctoral or law degrees. As can be seen from this original coding, the “amount of education” scale was not well balanced, and it was biased toward high school dropouts. In an attempt to address this issue, we recoded the amount of education variable as follows. Scores 0 to 4 became “1” (high school dropout), Scores 5 to 6 became “2” (high school graduate, no college), Score 7 became “3” (high school graduate, some college), Scores 8 and 9 became “4” (college degree), Scores 10 and 11 became “5” (master’s degree), and Score 12 became “6” (PhD). Using this new coding scale, the mean of amount of education in the sample (N � 81,075) was 3.07, with a standard deviation of 1.17, which means that the average person in the sample, at Time 2 (i.e., 11 years after being first surveyed in high school), had graduated high school and had some college experi- ence, but no college degree. This new scale also implies that, on average, the distance between each scale unit translates into about 2 years of education.

Annual income. At the 11th-year follow-up (Time 2), partic- ipants reported their rate of pay per month, per week, or per hour. Their responses were coded by the Project Talent staff to estimated annual income (see Wise et al., 1979). We converted these scores into the natural logarithm of annual income, which is a strategy often used to normalize the highly skewed distribution of income. Using the natural logarithm of income also facilitates the interpre- tation of results, because the value of the unstandardized beta (when b � .10) is equivalent to percentage increases in income (upon a unit increase in the predictor). Logarithmic annual income of the participants ranged from 2.48 to 12.25, and the dollar amounts were not adjusted for inflation prior to computing the logarithm, so they represent 1971 values. However, all dollar amounts reported in the text of this article were adjusted for inflation to 2014 values.

Occupational prestige. Occupational prestige refers to the social status of a specific occupation, as regarded by members of a society (Hauser & Warren, 1997). A widely used measure of occupational prestige is Stevens and Featherman’s (1981) Total Socioeconomic Index (TSEI2), which was derived from the Dun- can Socioeconomic Index (SEI; O. D. Duncan, 1961), by updating it to encompass the 1970 census job titles (U.S. Bureau of Census, 1971). The 11th-year follow-up of Project Talent (Time 2) in- cluded self-reported job titles. These job titles were coded by the Project Talent staff into the 1970 census occupation codes, based on which the second author assigned TSEI2 scores (Su, 2012) following the coding system provided by Stevens and Featherman (1981). Each job title from the 1970 census has a specific prestige score assigned, based on expert ratings. In our sample, prestige scores ranged from 16.46 (professional driver) to 89.57 (dentist), with a mean of 47.3 (sales representative). To facilitate the inter-

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477PERSONALITY, SOCIAL BACKGROUND, AND ATTAINMENT

pretation of our results, we use this raw metric and its matching job titles throughout the article.

Data Cleaning

Participants were excluded prior to all analyses based on two factors: response credibility and missing data. Regarding response credibility, we only analyzed cases that were coded as “credible” on the original response credibility index (see Wise et al., 1979). This credibility index was computed based on a Screening scale, which included questions such as “How many days are in a week?” that should have been answered easily by anyone who did not suffer from a reading problem, a clerical problem in recording answers, general slowness, or a lack of cooperation. Missing data were handled throughout the analyses using listwise deletion. Out of about 377,000 cases available at Time 1, about 346,000 were credible and not missing according to the response credibility index. Furthermore, in most analyses, the sample was reduced by the fact that at Time 2, we have about 81,000 participants. We present an attrition analysis in the results section.

Data Analysis

The main analyses consist of moderated polynomial regression analyses (see Edwards, 2002), in which each of the three attain- ment variables at Time 2 (educational attainment, annual income, and occupational prestige) was an outcome. The predictors were the following: each individual difference variable (Big Five per- sonality dimension or intelligence; simple and squared), parental SES (simple and squared), the interaction between the respective individual difference variable and parental SES, gender, race, and age cohort. In addition, all the regressions were weighted by the case weights available at Time 2, to produce parameter estimates closer to those obtained from a representative sample. Note that because of multicollinearity among the personality variables (see Table 1), we examined the effect of each individual difference on status attainment in a separate regression. We chose the polyno- mial regression approach because it both tests the proposed models and it allows for a more informed interpretation of the simple, nonlinear, and multiplicative relationships among the focal vari- ables (Edwards, 2002). One of the primary interpretive advantages of polynomial regression analysis is the ability to translate the findings into response surface analysis and three-dimensional graphs (see Edwards, 2002; Shanock et al., 2010).

Although this method has been mainly been used in the past to examine self-observer rating discrepancies, it may be used to describe any interaction effects, as long as the predictor variables are measured on the same scale or are standardized (as it was the case with our variables; Shanock et al., 2010). To test our hypoth- eses (independent effects, resource substitution, and Matthew ef- fect), we first established whether the interaction effects were statistically significant, and when that was the case, we constructed response surface graphs to help us better understand the meaning and the magnitude of the effects. In addition to the three- dimensional graphs, we also obtained four surface parameters, which may be used to further explore the results and answer a variety of interesting questions. The first two parameters (a1 and a2) refer to the slope and curvature of the line of perfect agree- ment, as relating to the outcome (i.e., in our case, the line of perfect

agreement represents the line on the graph in which individual differences and parental SES are equal in magnitude, and the surface tests tell us how that agreement relates to the outcomes). For example, a positive (a1) slope on the line of perfect agreement would indicate that status attainment increases as both SES and personality increase. A positive (a2) curvature on this line of perfect agreement would indicate a convex (upward curving) sur- face, whereas a negative (a2) curvature would indicate a concave (downward curving) surface (i.e., outcomes could increase or decrease more sharply as both personality and SES become lower or higher form some point). The second two parameters (a3 and a4) refer to the slope and curvature of the line of discrepancy as relating to the outcome (i.e., in our case, the line of discrepancy represents the line on the graph in which personality and SES are opposite in magnitude, and the surface tests tell us how that discrepancy relates to the outcomes). For example, a negative (a3) slope on the line of perfect discrepancy, in which personality equals negative parental SES, would indicate that status attainment increases as the discrepancy between SES and personality in- creases such that SES is higher than personality. A positive (a4) curvature on this line of discrepancy would indicate a convex surface (i.e., outcomes would increase more sharply as the dis- crepancy between personality and SES increases).

In addition to the above analyses, and to test the robustness of our findings, we reestimated all the models (i.e., one independent model for each personality trait variable, on each of the three outcomes), including intelligence (simple and squared) and the interaction between intelligence and parental SES, as predictors in the regression models. To establish optimal status attainment mod- els, we also provided regression model comparisons (between models with and without controls). These robustness tests were necessary especially in light of new findings by Major, Johnson, and Deary (2014), who showed that intelligence and personality traits are related in the Project Talent sample, and that it is important to consider them together in predictive models.

Finally, given our large participant sample and the sensitivity of null hypothesis significance testing (NHST) to participant sample size, almost all our analyses resulted in significant results at p � .001. Furthermore, confidence intervals were often so close to the respective parameters that they provide little valuable information. That is to say, the confidence intervals were so small that if the parameters themselves were numerically different, they were also outside of the respective confidence intervals of each estimate. Thus, the typical approaches to evaluating findings (e.g., statistical significance, or parameter estimates with confidence intervals) were not that useful. As an alternative, we took advantage of the fact that our outcomes were on naturally meaningful metrics (e.g., years of schooling, income). Wherever possible, we have trans- lated the main and interaction effects into the natural metric of the outcome variable, which allows readers to decide for themselves whether our effects are sizable enough to be of interest (for an extensive discussion and recommendations to shift from NHST to effect size interpretation, see Cumming, 2014).

Results

Table 1 presents intercorrelations among all the variables of interest. Table 2 presents results from an attrition analysis. Of the approximately 346,000 participants available at Time 1, about

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478 DAMIAN, SU, SHANAHAN, TRAUTWEIN, AND ROBERTS

81,000 responded at Time 2. The attrition analysis showed that the participants who stayed in the study, as opposed to those who dropped from the study, were more intelligent (r � .23) and had slightly higher parental SES (r � .11), but were not very different in their Big Five levels (average effect was .04). Furthermore, the gender and race distributions were very similar across time points: Time 1 had 51.6% females and 95.5% Whites/Caucasians, whereas Time 2 had 52.3% females and 96.2% Whites/Caucasians. Given these results, the Project Talent sample available at Time 2 cannot be considered a representative sample of the U.S. population,

unlike the Project Talent sample available at Time 1. However, by using weighted least squares regressions using the case weights available at Time 2, the estimates from our models can still be considered to be close to those drawn from a nationally represen- tative sample, and it remains one of the largest in the world for which prospective effects of personality traits and cognitive abil- ities on education, income, and job prestige can be evaluated.

Prospective Effects of Personality Traits on Status Attainment

Educational attainment. We first tested the predictive re- lation between the personality traits and educational attainment without factoring in intelligence, which was closer to the ap- proach taken by Shanahan and colleagues (2014). Table 3 (Model 1) presents the moderated polynomial regression results for each of the Big Five traits (i.e., one independent regression model for each personality trait). All the main effects of the Big Five personality traits and parental SES were statistically sig- nificant at p � .001. Of the five interactions (between the Big Five traits and parental SES), three were statistically signifi- cant, namely, Extraversion, Agreeableness, and Conscientious- ness. Because inferential statistics fail to provide us with a meaningful interpretation of these results, we provide effect size estimates translated into natural metrics (see Table 4). For

Table 2 Attrition Analyses

Variable Mean difference 95% CI r

Parental SES �.22 [�.23, �.22] �.11 Extraversion �.04 [�.05, �.03] �.02 Agreeableness �.05 [�.06, �.05] �.03 Conscientiousness �.13 [�.14, �.13] �.07 Emotional Stability �.11 [�.12, �.10] �.06 Openness �.06 [�.07, �.05] �.03 Intelligence �.46 [�.46, �.45] �.23

Note. Negative effects mean higher scores for the people who stayed in the study. All variables were standardized prior to the analyses. CI � confidence interval; SES � socioeconomic status.

Table 3 Moderated Polynomial Regression Models for the Educational Attainment Outcome (One Independent Regression for Each Personality Trait)

Model Educational attainment

predictors Extraversion Agreeableness Conscientiousness Emotional stability Openness

1 Personality .10 .11 .15 .11 .14 Parental SES .46 .46 .46 .46 .45 Personality � Parental SES �.03 �.03 �.02 �.01 �.00 Personality2 .05 .00 .05 .02 .01 Parental SES2 .08 .08 .08 .08 .07 Gender �.22 �.24 �.23 �.22 �.24 Race �.10 �.10 �.10 �.10 �.09 Age cohort .04 .03 .03 .03 .03 Constant 2.76 2.8 2.75 2.78 2.79 R2 .24 .24 .25 .25 .25

2 Personality .09 .08 .10 .06 .11 Parental SES .28 .29 .29 .29 .28 Personality � Parental SES �.00 �.01 �.00 �.00 .01 Personality2 .02 �.01 .01 .00 �.01 Parental SES2 .05 .05 .05 .05 .05 Gender �.15 �.16 �.16 �.15 �.17 Race �.25 �.25 �.25 �.25 �.25 Age cohort �.08 �.08 �.08 �.08 �.08 Intelligence .49 .48 .48 .48 .48 Intelligence2 .03 .03 .03 .03 .03 Intelligence � Parental SES .04 .04 .04 .04 .04 Constant 2.89 2.92 2.90 2.92 2.92 R2 .39 .38 .39 .38 .39

Comparison �R2 .14 .14 .13 .13 .14 N 74,644 74,644 74,644 74,644 74,644

Note. Model 1 controls for gender, race, and age; Model 2 adds controls for IQ, IQ-square, and IQ � SES. Unstandardized regression coefficients. Gender and race are effects-coded (men � �1, women � 1; non-Whites��1, Whites � 1). Personality traits, intelligence, parental SES, and age cohort were standardized prior to the analyses. All regression analyses were weighted by the 11th-year case weight assigned by the Project Talent staff to account for attrition. Bold font indicates p � .001 (alpha level was set based on a Bonferroni correction to account for the number of analyses conducted across this data set). SES � socioeconomic status.

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479PERSONALITY, SOCIAL BACKGROUND, AND ATTAINMENT

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480 DAMIAN, SU, SHANAHAN, TRAUTWEIN, AND ROBERTS

example, moving up 1 standard deviation on extraversion is associated with gaining an extra 1.8 months of education, whereas going from 1 standard deviation below the mean to 1 standard deviation above the mean results in 3.6 months of education gained by Year 11. Parental SES, which has a larger main effect on educational attainment, results in 16.6 months (about 2 academic years) of education gained by Year 11 when moving 2 standard deviations.

To further investigate the interaction effects between the Big Five traits and parental SES in predicting educational attain- ment 11 years later, we used response surface methodology and constructed three-dimensional graphs of the three interactions that were statistically significant. Because the pattern we found was very similar across Extraversion, Agreeableness, and Con- scientiousness, we only present the results for Agreeableness (see Figure 1). To address our hypotheses (independent effects, resource substitution, or Matthew effect), we need to compare the effects that Agreeableness had on educational attainment at different levels of parental SES. Examining the graph, we can see that Agreeableness had a slightly stronger effect on educa- tional attainment at low (as opposed to high) levels of parental SES. Thus, when parental SES was the highest ( 2 SD), going from �2 SD to 2 SD in Agreeableness gained people a .20 unit increase in education, whereas when parental SES was the lowest (�2 SD), going from �2 SD to 2 SD in Agreeableness gained them .68 units increase in education. This translates to a net advantage of Agreeableness of .48, or the equivalent of 8.6 additional months of education (about 1 academic year) at low (vs. high) levels of SES. Therefore, Agreeableness showed evidence for resource substitution.

This pattern replicated for extraversion and conscientiousness. They all showed evidence for resource substitution, in which an increase in extraversion and conscientiousness benefited people more at low (vs. high) levels of parental SES.

Annual income. Table 5 (Model 1) presents the moderated polynomial regression results (separate regressions for each per- sonality trait). All the main effects of the Big Five personality traits and parental SES were statistically significant at p � .001. Of the interaction effects, extraversion and conscientiousness showed sta-

tistically significant interactions with parental SES. The largest main effect of the Big Five traits was for extraversion (b � .04), which means that there was a 17% difference in income between people who were two standard deviations below (vs. above) the mean on extraversion. As Table 4 shows, at the average income, this percentage difference in annual income translates to $9,674 (adjusted for inflation to 2014 purchasing power). Parental SES had an average main effect on annual income of b � .07, which translates to a 32% difference in income (going from �2 SD to 2 SD in parental SES), which, at the average income, is the equiv- alent of $16,987.

Regarding the interaction effects, both extraversion and consci- entiousness showed evidence for resource substitution, whereby higher personality trait levels benefited people’s annual income more at lower (as opposed to higher) levels of parental SES. Figure 2 shows the effect for conscientiousness. Examining the graph, we can see that when parental SES was the highest ( 2 SD), going from �2 SD to 2 SD in conscientiousness gained people a 4% increase in annual income, whereas when parental SES was the lowest (�2 SD), going from �2 SD to 2 SD in conscientiousness gained them a 22% increase in income. This translates to a net advantage of conscientiousness of about 18% in annual income at low (vs. high) levels of parental SES. Therefore, conscientiousness showed evidence for resource substitution and this effect repli- cated for extraversion.

Occupational prestige. Table 6 (Model 1) presents the mod- erated polynomial regression results (separate regressions for each personality trait). All the main effects of the Big Five personality traits and parental SES were statistically significant at p � .001. Of the five interaction effects with parental SES, only Extraversion was statistically significant. To better understand the meaning of the effects, we provide effect size estimates translated in natural metrics (see Table 4; additionally, for a TSEI2 prestige scale sample with matching job titles, see Table 7).

To further investigate the interaction effect with parental SES, we constructed a response surface graph for extraversion (see Figure 3). Examining the graph, we can see that extraversion had a slightly stronger effect on occupational prestige at low (as opposed to high) levels of parental SES. Thus, when parental SES

Figure 1. Agreeableness and socioeconomic status (SES) predicting educational attainment (without IQ controls, corresponding to Table 3, Model 1). See the online article for the color version of this figure.

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481PERSONALITY, SOCIAL BACKGROUND, AND ATTAINMENT

was the highest ( 2 SD), going from �2 SD to 2 SD in extraversion gained people only 3.5 prestige points, whereas when parental SES was the lowest (�2 SD), going from �2 SD to 2 SD in extraversion gained them 12.8 prestige points. This trans-

lates to a net advantage of extraversion of 9.26 prestige points (i.e., the equivalent of going from a “mail handler” to a “retail sales- man”) at low (vs. high) levels of SES. Therefore, extraversion showed evidence for resource substitution.

Table 5 Moderated Polynomial Regression Model for the Annual Income Outcome (One Independent Regression for Each Personality Trait)

Model Income predictors Extraversion Agreeableness Conscientiousness Emotional Stability Openness

1 Personality .04 .02 .03 .03 .02 Parental SES .07 .07 .07 .07 .07 Personality � Parental SES �.01 �.00 �.01 �.00 �.00 Personality2 .00 .01 .00 .00 .00 Parental SES2 .00 .00 .00 .00 .00 Gender �.33 �.34 �.33 �.33 �.34 Race �.02 �.02 �.02 �.02 �.02 Age cohort �.07 �.07 �.07 �.07 �.07 Constant 9.01 9.00 9.01 9.01 9.01 R2 .30 .30 .30 .30 .30

2 Personality .03 .02 .02 .03 .02 Parental SES .05 .06 .06 .06 .06 Personality � Parental SES �.01 �.00 �.01 .00 �.00 Personality2 �.00 .01 �.00 �.00 �.00 Parental SES2 .01 .01 .01 .01 .01 Gender �.33 �.33 �.33 �.33 �.33 Race �.04 �.04 �.04 �.04 �.04 Age cohort �.08 �.08 �.08 �.08 �.08 Intelligence .05 .05 .05 .05 .05 Intelligence2 .00 .00 .00 .01 .00 Intelligence � Parental SES �.01 �.01 �.01 �.01 �.02 Constant 9.02 9.02 9.02 9.02 9.02 R2 .31 .31 .31 .31 .31

Comparison �R2 .01 .01 .01 .00 .01 N 53515 53515 53515 53515 53515

Note. Model 1 controls for gender, race, and age; Model 2 adds controls for IQ, IQ-square, and IQ � SES. Unstandardized regression coefficients. Gender and race are effects-coded (men � �1, women � 1; non-Whites��1, Whites � 1). Personality traits, intelligence, parental SES, and age cohort were standardized prior to the analyses. All regression analyses were weighted by the 11th-year case weight. Bold font indicates p � .001. SES � socioeconomic status.

Figure 2. Conscientiousness and socioeconomic status (SES) predicting the natural logarithm of annual income (without IQ controls, corresponding to Table 5, Model 1). See the online article for the color version of this figure.

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482 DAMIAN, SU, SHANAHAN, TRAUTWEIN, AND ROBERTS

Prospective Effects of Intelligence on Status Attainment

Table 8 shows the prospective effects of intelligence on each of the three status attainment outcomes. Intelligence had significant main effects on all three outcomes. Thus, people who were two standard deviations above (as opposed to below) average in IQ gained 35.3 months (i.e., 4 academic years) of education, $12,094, and 33.96 occupational prestige points, going from an “electric power lineman” to a “health administrator.” Parental SES also had significant main effects on all the outcomes, but the effect was diminished compared with the effect of parental SES in the per- sonality regressions, for which IQ was not controlled for. Further- more, intelligence showed a significant interaction effect with parental SES when predicting all three outcomes. Specifically, intelligence showed evidence for a Matthew effect when predict- ing educational attainment and occupational prestige, and a re- source substitution effect when predicting income. Figure 4 shows three-dimensional graphs of the two interaction patterns found between intelligence and SES.

Examining the educational attainment graph in Figure 4, we can see that intelligence had a stronger effect on educational attainment at high (as opposed to low) levels of parental SES. Thus, when parental SES was the highest ( 2 SD), going from �2 SD to 2 SD in intelligence gained people a 2.28 units increase in education, whereas when parental SES was the lowest (�2 SD), going from �2 SD to 2 SD in intelligence gained them 1.64 units increase in education. That translates to

a net advantage of intelligence .64, or the equivalent of 11.5 months of additional education (i.e., more than a year) at high versus low levels of SES, which is indicative of a Matthew effect. This pattern replicated for occupational prestige, intel- ligence showing a larger prestige advantage at higher (vs. lower) levels of parental SES.

Examining the annual income graph of Figure 4, we can see that intelligence had a stronger effect on annual income at low (as opposed to high) levels of parental SES. Thus, when parental SES was the highest ( 2 SD), going from �2 SD to 2 SD in intelligence gained people a 4% increase in annual income, whereas when parental SES was the lowest (�2 SD), going from �2 SD to 2 SD in intelligence gained them a 43% increase in annual income. This translates to a net advantage of intelligence of 39% in annual income at low (as opposed to high) parental SES, which is evidence for resource substitu- tion.

Furthermore, as can be seen from Tables 3, 5, and 6 (Models 2), the main effects of intelligence, as well as the interaction effects between intelligence and parental SES, on educational attainment, annual income, and occupational prestige were highly robust. These effects remained virtually unchanged when individual personality trait controls were included in the regres- sion models. The effects of intelligence also remained un- changed when controlling for all personality traits and their interactions with SES simultaneously.

Table 6 Moderated Polynomial Regression Models for the Occupational Prestige Outcome (One Independent Regression for Each Personality Trait)

Model Prestige predictors Extraversion Agreeableness Conscientiousness Emotional Stability Openness

1 Personality 2.03 2.10 3.01 2.21 2.29 Parental SES 6.87 6.98 6.92 6.89 6.88 Personality � Parental SES �.58 �.26 �.16 �.20 �.18 Personality2 .86 .33 .66 .61 .39 Parental SES2 .53 .99 1.01 .95 .84 Gender �2.17 �2.53 �2.41 �2.16 �2.06 Race .13 .12 .18 .05 .28 Age Cohort .40 .30 .21 .20 .34 Constant 39.96 40.50 40.08 40.32 40.32 R2 .15 .15 .16 .16 .15

2 Personality 1.93 1.61 2.01 1.15 2.02 Parental SES 3.88 4.01 4.11 4.09 3.90 Personality � Parental SES �.20 .03 .06 �.01 .18 Personality2 .33 .03 �.05 .18 .01 Parental SES2 .59 .54 .60 .56 .49 Gender �1.25 �1.50 �1.37 �1.20 �1.63 Race �2.64 �2.67 �2.52 �2.71 �2.54 Age Cohort �1.71 �1.76 �1.71 �1.71 �1.77 Intelligence 8.38 8.36 8.19 8.27 8.39 Intelligence2 .63 .62 .60 .56 .62 Intelligence � Parental SES .37 .39 .38 .38 .31 Constant 42.00 42.34 42.30 44.22 42.26 R2 .28 .28 .28 .27 .28

Comparison �R2 .12 .12 .12 .12 .12 N 53,538 53,538 53,538 53,538 53,538

Note. Model 1 controls for gender, race, and age. Model 2 adds controls for IQ, IQ-square, and IQ � SES. Unstandardized regression coefficients. Gender and race are effects-coded (men � �1, women � 1; non-Whites��1, Whites � 1). Personality traits, intelligence, parental SES, and age cohort were standardized prior to the analyses. All regression analyses were weighted by the 11th-year case weight. Bold font indicates p � .001. SES � socioeconomic status.

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483PERSONALITY, SOCIAL BACKGROUND, AND ATTAINMENT

Because intelligence revealed such large main effects on all three outcomes, and because it showed statistically significant and robust interaction effects with parental SES when predict- ing all three outcomes, we considered it necessary to include it as a control (along with its square and its interaction term with SES) in the personality regressions predicting status attainment. A second reason why we considered these analyses necessary is that previous research (Major et al., 2014) found that intelli- gence and personality were related in the Project Talent data set, and recommended that status attainment models should include both predictors. Finally, previous research on status attainment that investigated the interactive effects between per- sonality traits and background factors did not include intelli- gence as a statistical control (see Shanahan et al., 2014). The goal of our article was to extend the previous cross-sectional findings by Shanahan and colleagues (2014) to a longitudinal

data set, and to test the robustness of these findings by includ- ing intelligence controls. Thus, we conducted several additional analyses, which we discuss next (the results can be found in Models 2 of Tables 3, 5, and 6).

Prospective Effects of Personality Traits on Status Attainment (Controlling for Intelligence)

Educational attainment. As can be seen from Table 3, in Model 2, we reestimated the five personality trait regression mod- els, controlling for intelligence (simple and squared) and the in- teraction between intelligence and SES. Model 2 had a signifi- cantly better fit than Model 1, across the Big Five traits (the average �R2 � .14, p � .001). The main effects of the Big Five remained significant, and the effect sizes did not change dramat- ically. The main effects of SES also remained significant, but the average effect size was reduced by about 50% in Model 2 com- pared with Model 1. However, the interaction effects between Extraversion, Agreeableness, Conscientiousness and parental SES were no longer statistically significant. Thus, none of the Big Five personality traits showed evidence for resource substitution in

Table 7 Sample of TSEI2 Occupational Prestige Scores Matched With Job Titles, Following the Coding System Provided by Stevens and Featherman (1981)

Prestige score Job title

16.5 (lowest score in the sample) Professional drivers 26.0 (�1 SD) Mail handlers, except post office 28.0 Compositors and typesetters 28.1 Electric power linemen and

cablemen 28.5 Telegraph operators 29.0 Receptionists 30.0 Stenographers 30.5 Locomotive firemen 30.8 Inspectors 32.2 Decorators and window dressers 32.7 Railroad conductors 34.4 Auctioneers 34.7 Legal secretaries 35.3 Salesmen, retail trade 38.4 Managers and superintendents

buildings 39.6 Construction inspectors 40.1 Managers and administrators 44.1 Clerical supervisors 44.9 Inspectors, except construction,

public administration 47.3 (Mean) Sales representatives 49.1 Data processing machine

repairmen 49.6 Foresters and conservationists 50.1 Air traffic controllers 51.6 High school teachers 52.5 Officials of lodges, societies,

and unions 53.4 Insurance agents, brokers, and

underwriters 54.5 Purchasing agents and buyers 57.1 Officials and administrators 61.9 Health administrators 64.9 Real estate appraisers 68.6 ( 1 SD) Airplane pilots 77.8 Chemists 89.6 (highest scores in the sample) Dentists

Note. These prestige scores are representative of the 1970 census job titles (U.S. Bureau of Census, 1971), which is when our participants were assessed for the 11th-year follow-up.

Table 8 Moderated Polynomial Regressions of Each of the Three Status Attainment Outcomes

Predictors Educational attainment

Annual income

Occupational prestige

Intelligence .49 .05 8.49 Parental SES .30 .06 4.21 Intelligence � Parental SES .04 �.02 .36 Intelligence2 .03 .00 .56 Parental SES2 .05 .01 .60 Gender �.14 �.32 �1.12 Race �.26 �.04 �2.81 Age cohort �.07 �.08 �1.59 Constant 2.92 9.02 42.53 R2 .38 .31 .27 N 74,644 53,515 53,538

Note. Unstandardized regression coefficients. Gender and race are effects- coded (men � �1, women � 1; non-Whites��1, Whites � 1). Intelli- gence, parental SES, and age cohort were standardized prior to the anal- yses. All regression analyses were weighted by the 11th-year case weight. Bold font indicates p � .001. SES � socioeconomic status.

Figure 3. Extraversion and socioeconomic status (SES) predicting occu- pational prestige (without IQ controls, corresponding to Table 6, Model 1). See the online article for the color version of this figure.

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484 DAMIAN, SU, SHANAHAN, TRAUTWEIN, AND ROBERTS

predicting educational attainment, when controlling for intelli- gence.

Annual income. As can be seen from Table 5, Model 2 (in which we controlled for intelligence, its square, and its interaction with SES, in each of the five personality trait regressions) did not fit much better than Model 1 across the Big Five traits (although the average �R2 � .01 was statistically significant). The main effects of the Big Five remained significant, and the effect sizes did not change by much. The main effects of SES also remained significant, and the average effect did not change much. Of the two interaction effects that were previously statistically significant, Conscientiousness continued to show evidence for resource sub- stitution when interacting with parental SES and the effect did not change when controlling for intelligence. Thus, being higher in Conscientiousness benefited more the annual income of people at lower versus higher levels of parental SES.

Occupational prestige. As can be seen from Table 6, in Model 2, we reestimated the five personality trait regression mod- els, controlling for intelligence (simple and squared) and the in- teraction between intelligence and SES. Model 2 had a signifi- cantly better fit than Model 1, across the Big Five traits (the average �R2 � .11, p � .001). The main effects of the Big Five and SES remained significant, although the effects changed some- what. However, the interaction effect between extraversion and parental SES was no longer statistically significant. Thus, none of the Big Five personality traits showed evidence for interactive effects with parental SES, when predicting occupational prestige in the presence of intelligence controls.

Discussion

Previous research has investigated either the role of individual differences in personality traits and cognitive ability, the role of SES, or the added value of one predictor over the others on status attainment. In this article, we tested the possibility that background factors, such as parental SES, moderated the role of individual

differences on status outcomes, and that personality traits and intelligence may compensate for background disadvantage. Spe- cifically, we tested whether individual differences in personality traits and intelligence followed one of three patterns in relation to parental SES when predicting attained status: (a) the independent effects hypothesis (i.e., personality characteristics and intelligence predict attainments independent of parental SES level), (b) the resource substitution hypothesis (i.e., personality characteristics and intelligence are stronger predictors of attainments at lower levels of parental SES), and (c) the Matthew effect hypothesis (i.e., “the rich get richer”; personality characteristics and intelligence are stronger predictors of attainments at higher levels of parental SES).

Although we did find some evidence for both the resource substitution and the Matthew effect hypotheses, especially for intelligence, the most robust pattern across all models supported the independent effects hypothesis. Our findings showed that per- sonality traits had meaningful main effects on educational attain- ment, income, and occupational prestige, even after controlling for SES and intelligence (e.g., Noftle & Robins, 2007; Poropat, 2009; Sutin, Costa, Miech, & Eaton, 2009). Table 4 presents all the main effect sizes in natural metrics. Similarly, we replicated previous findings that intelligence positively predicts status attainment (ed- ucational attainment, annual income, and occupational prestige), even when controlling for SES and personality traits (e.g., Batty et al., 2009; Cawley et al., 2001). Not surprisingly, parental SES was also found to be a relatively strong predictor of status attainment, across all three outcome variables, which is also in line with previous research (G. J. Duncan & Brooks-Gunn, 1997; O. D. Duncan et al., 1972).

Without controlling for intelligence, we did find evidence of resource substitution for Extraversion, Agreeableness, and Consci- entiousness when predicting educational attainment, for Extraver- sion and Conscientiousness when predicting annual income, and for Extraversion when predicting occupational prestige. However,

Figure 4. IQ and socioeconomic status (SES) predicting educational attainment and income (without person- ality controls, corresponding to Table 8). See the online article for the color version of this figure.

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485PERSONALITY, SOCIAL BACKGROUND, AND ATTAINMENT

of these effects, only the interaction between Conscientiousness and parental SES when predicting annual income survived our robustness test, which included intelligence controls.

We found more robust interactions between intelligence and parental SES on all three outcomes. Specifically, intelligence showed evidence for a Matthew effect (“the rich get richer”) when predicting educational attainment and occupational prestige, in which intelligence benefited people at higher SES levels more, and it showed evidence for resource substitution when predicting an- nual income, in which intelligence benefited people at lower SES levels more. These effects did not change when including person- ality controls in the regressions.

The initial resource substitution effects of personality traits replicated and extended previous findings by Shanahan and col- leagues (2014), who found evidence of resource substitution on educational attainment for Agreeableness, Openness, and Emo- tional Stability in a cross-sectional study. Although some of our findings overlapped with the cross-sectional study by Shanahan and colleagues, some did not; for instance, we did not find evi- dence of resource substitution for Openness and Emotional Sta- bility, but we did for Extraversion and Conscientiousness. Never- theless, there are many differences between the present study and the study by Shanahan and colleagues that may explain these discrepancies: (a) we used parental SES as a moderator, whereas they used parental education as a moderator; (b) we used Big Five composites derived from the Project Talent personality scales, whereas they used the Mini-IPIP, which is a 20-item short-form version of the International Personality Item Pool (IPIP) designed to measure the Big Five factors of personality (Donnellan, Oswald, Baird, & Lucas 2006); (c) we used a prospective longitudinal design, whereas they used a cross-sectional design; and (d) we used moderated polynomial regressions and response surface anal- ysis, whereas they used moderated linear regressions.

In sum, the present study replicated, in a longitudinal setting, some of the cross-sectional resource substitution effects previously found by Shanahan and colleagues (2014), and extended this research by further testing the robustness of these effects, when including intelligence controls. When subjected to these tests, most interactive effects between personality and parental SES ceased to be statistically significant, thus indicating that an independent effects model is more appropriate. This conclusion, however, did not apply to intelligence, for which Matthew effects and resource substitution effects were larger and more robust. One open ques- tion, however, is why intelligence showed different interaction patterns with parental SES when predicting different outcomes. One possibility is that intelligence showed a Matthew effect when predicting educational attainment and occupational prestige be- cause the two outcomes are highly correlated (see Table 1) and heavily dependent on access to higher education; the latter has a high entry cost in the United States because of college tuition and fees, which wealthier families are better able to afford. This might explain why being intelligent benefits more the educational attain- ment and occupational prestige of people from higher (vs. lower) SES backgrounds. When predicting annual income, intelligence showed a resource substitution pattern, whereby it helped people from lower SES backgrounds more. This could be so because there are more paths to a higher income, which might not require entry-level costs as high as the cost of a college education. Future research should test these hypotheses to disentangle the

reasons behind the distinct interaction patterns between intelli- gence and parental SES.

One advantage of the present article is that we used response surface analysis to interpret and visualize the interactions in a three-dimensional space. Thus, the graphs, along with the surface tests, provide additional information, which can help answer in- teresting theoretical and practical questions. One such question is, “Given the evidence for resource substitution, does that mean the ‘American Dream’ is alive and well?” In other words, do certain personality traits, such as being nice to others, or working hard, compensate for background disadvantage? In terms of the Amer- ican Dream, the question really becomes: Can people who are born into poverty “catch up” to their peers born into affluence through their personality traits or intelligence? Compensation, in this case, is not necessarily reflective solely of an interaction pattern as much as the combination of main effects and interaction effects on outcomes. For example, if a personality trait like conscientiousness had a main effect as large as, if not larger than parental SES, then it could be said to “compensate” for parental SES such that people brought up in poverty who were highly conscientious could do just as well as those low in conscientiousness who were brought up in wealth. Of course, if the interaction effects between personality and parental SES were large, they, too, could contribute to the possibility that individual differences could make up for back- ground disadvantage. One benefit of the response surface figures is that one can see, in practical terms, whether individual differences can make up for a disadvantaged background.

In the case of personality traits, neither the main effects nor the interactive effects were large enough to compensate for low pa- rental SES, and this can be seen on the response surface figures. For example, even in the ideal case of the interaction between Agreeableness and parental SES, when cognitive ability was not controlled for, being highly agreeable did not make up for low parental SES. For example, Figure 1, which shows the relation of both Agreeableness and parental SES with educational attainment, supports this conclusion. The slope of the line of perfect disagree- ment (a3, running from the left to the right side of the graph, in which personality equals negative parental SES) is the most ap- propriate test for this question, and its negative value indicates that educational attainment increases as the discrepancy between SES and Agreeableness increases such that SES is higher than Agree- ableness. Examining the left and right corners of the figures, we see that the most agreeable (but poorest) people did not earn (on average) more than a high school degree, whereas the least agree- able (but wealthiest) people earned college degrees. In the case of the one personality interaction that survived our robustness test, the most conscientious (but poorest) people did not attain (on average) an annual income higher than 8.97 on the natural loga- rithm scale (i.e., $46,256 adjusted for inflation to 2014), whereas the least conscientious (but wealthiest) people attained annual incomes of 9.13 on the natural logarithm scale (i.e., $54,286 adjusted for inflation to 2014). Therefore, personality traits, al- though important in the prediction of attainment outcomes, did not suffice to make up for low parental SES.

However, the story was different for intelligence. There, the slope of the line of perfect disagreement was positive for educa- tional attainment and occupational prestige (and flat for income), indicating that as the discrepancy increased (such that IQ was higher), educational attainment and occupational prestige in-

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486 DAMIAN, SU, SHANAHAN, TRAUTWEIN, AND ROBERTS

creased. In other words, the smartest but least wealthy people were close to getting a college degree, whereas their least smart but wealthiest counterparts were close to getting an associate’s degree. In sum, even though we have evidence that certain personality traits may compensate for background disadvantage (in the ab- sence of intelligence controls), the effects were not large enough to overcome the main effect of SES. The only individual difference that seemed to be able to do that was intelligence. Thus, we would conclude that the American Dream, as manifest through personal- ity, is more myth than fact. On the other hand, the American Dream manifest through intelligence is still alive and well. Al- though, it should be noted that the descriptions of the American Dream seldom mention cognitive ability as the compensatory factor that would make up for starting out life in difficult straits.

Another interesting theoretical question that may be addressed using the response surface tests is, “Should we switch from an independent effects model to an interaction effects model when predicting status attainment?” Examining the a1 parameters across Figures 1 through 4 may answer that question. These parameters represent the slope of perfect agreement (i.e., the sum of the main effects of individual differences and SES), in other words, the independent effects model. This slope was consistently positive, indicating that increasing levels of both personality traits (or intelligence) and parental SES were beneficial for status attain- ment. The main effects were also quite robust when adding con- trols, and they were consistently larger than the interaction effects. Thus, we would conclude that although some interactive effects were present between personality traits and parental SES (when not taking into account intelligence), the magnitude of these effects and their lack of robustness do not always justify their inclusion, especially in smaller samples. The independent effects model seems to do a good job predicting status attainment in a robust fashion. Regarding intelligence, on the other hand, models of status attainment that take into account interactions between intel- ligence and background factors seem warranted.

Finally, by investigating the prospective interactive effects of individual differences and background factors on status attainment in a large representative sample, and by providing effect size estimates translated into meaningful raw metrics, the present arti- cle may have important practical implications on policy making. Knowing the effects of SES, intelligence, and personality in terms of dollars and in terms of months of schooling may allow other researchers to better compare effects of other variables or inter- ventions to these basic predictors. Knowing, for example, that an intervention to prepare students for college may net a gain similar to specific predictors like intelligence or personality could inform whether policy should be focused on selection strategies or inter- vention strategies given their respective costs and societal impli- cations.

One limitation of the present data is the age of testing. Namely, at Time 2, our participants were only 11 years (or less) out of high school, which means that, for many of them, the job they held at the follow-up was their first job, and thus not necessarily the most representative in terms of their full annual income potential or peak occupational prestige. Indeed, previous studies (Deary et al., 2005) showed that individual differences, such as intelligence, and SES were stronger predictors of annual income at midlife com- pared with young adulthood. To address this question, future studies should try to replicate the present findings on the latest

follow-up on the Project Talent sample, which was conducted 52 years after the original survey.

Another caveat is that most of the resource substitution effects failed to hold once intelligence was controlled. One possible explanation is that there is a developmental process that gets washed out over time. It is possible that at earlier ages, personality attributes are more highly valued by decision makers, such as teachers, that then give students more opportunities in the class- room. These opportunities may then enhance their abilities, which are then used more often for decision making when it comes to selecting students into higher education, for example. Although this is an interesting hypothesis and there is some evidence for it in previous studies (Becker, Lüdtke, Trautwein, Köller, & Baumert, 2012), the more typical mechanism is that educational opportunities impact school achievement rather than cognitive abilities. Another, more likely, explanation is that intelligence simply accounted for most of the variance when included in the models, thus diminishing the interactive effects between person- ality traits and parental SES.

A third limitation of these data is that they were collected more than 40 years ago, and the American education system and econ- omy have changed in important ways since then, which means that some of our effects might not replicate in more recent cohorts.

Fourth, as can be seen from Table 1 and as past research has pointed out (Major et al., 2014; Pozzebon et al., 2013), another limitation is that the PTPI does not differentiate very well between the Big Five personality traits, and thus we cannot be confident that the results would replicate with modern personality measures.2 Addition- ally, this study only used self-reports in order to assess personality traits. Future longitudinal studies should be designed with these fac- tors in mind, thus including better personality measures, both self- reports and other reports, to ensure measurement accuracy.

Fifth, our educational attainment scale might not be the true equiv- alent of an interval scale. In order to facilitate an interpretation focused on effect sizes in natural raw metrics, we estimated that, on average, each scale point represented 2 years of education (18 months). However, it is possible that there are qualitative differences between the different scale points (e.g., high school dropouts might be qualitatively different from the rest of the people), which our quan- titative scale did not capture. Future studies that are interested in qualitative differences may investigate such questions.

Despite these limitations, it should be noted that the present study tested the interactive effects of individual differences and parental SES on status attainment, using a prospective longitudinal design, measuring personality traits, intelligence, and parental SES in adoles- cence, and their prospective associations with attained status 11 years later. We also improved upon previous research by using three dif- ferent measures of status attainment— educational attainment, annual income, and occupational prestige—and by looking at the role of both

2 Because the Project Talent Personality Scales are not ideal for captur- ing the Big Five personality traits we factor analyzed the 10 scales using principal axis factoring with a varimax rotation and obtained two factors (Maturity and Extraversion). We reanalyzed the data using these two personality scales and we reached the same conclusions presented in this article. The independent effects model seems to better represent the rela- tions between personality and background factors when predicting stats attainment, whereas interactive models should be considered for intelli- gence. The detailed results of these analyses can be found at: https://osf .io/bsz8g/.

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487PERSONALITY, SOCIAL BACKGROUND, AND ATTAINMENT

personality traits and intelligence in the same sample. Furthermore, our study uses the largest nationally representative sample of U.S. high school students available, which leads to reliable estimates of effect sizes. Additionally, we tested the robustness of our findings by controlling for intelligence in the personality trait regressions. Finally, we provided meaningful interpretations of our results throughout the article, using raw natural metrics and focusing on effect size esti- mates, in an attempt to depart from traditional inferential statistics (especially because most effects are significant at p � .001 in such a large sample) and adopt the new statistics, which many researchers have argued are the key to a better science (Cumming, 2014).

Conclusion

We showed, in a longitudinal setting, in a large nationally repre- sentative sample of approximately 81,000 people, that personality traits and intelligence in adolescence, in addition to parental SES, matter in predicting status attainment in adulthood. A standard devi- ation increase in individual differences translated to up to 8 additional months (1 academic year) of education, $4,233 annually, and more prestigious occupations. Furthermore, we brought evidence for the resource substitution hypothesis, in which certain personality charac- teristics (e.g., Agreeableness, Conscientiousness, and Extraversion) were stronger predictors of attainments at lower levels of parental SES. However, all these effects (with the exception of the interaction between Conscientiousness and SES predicting income) were dimin- ished and became statistically nonsignificant when controlling for intelligence, suggesting that the independent effects model is more appropriate when predicting status attainment from personality traits and background factors.

We also showed that intelligence interacted with parental SES, when predicting all three outcomes, following two patterns: the Mat- thew effect when predicting educational attainment and occupational prestige, and resource substitution when predicting annual income. These effects remained statistically significant when controlling for personality traits.

This is the first longitudinal study to suggest that status attainment might be best predicted with more complex models that take into account the interactions between intelligence and background factors. However, this study also suggests that the independent effects model might be best suited when predicting status attainment from person- ality and background factors. Finally, we found that although person- ality traits may help compensate for background disadvantage to a small extent, they do not usually lead to a full “catch-up” effect. Intelligence was an exception, thus showing a full “catch-up” effect. To put it more colorfully, we would adapt George Carlin’s cynically humorous words (“Top 10 George Carlin Quotes,” 2008): “The reason they call it the American Dream is because you have to be asleep to believe it”— unless you happen to be extraordinarily intel- ligent.

References

Batty, G. D., Wennerstad, K. M., Smith, G. D., Gunnell, D., Deary, I. J., Tynelius, P., & Rasmussen, F. (2009). IQ in early adulthood and mor- tality by middle age: Cohort study of 1 million Swedish men. Epidemi- ology (Cambridge, Mass.), 20, 100 –109. http://dx.doi.org/10.1097/EDE .0b013e31818ba076

Becker, M., Lüdtke, O., Trautwein, U., Köller, O., & Baumert, J. (2012). The differential effects of school tracking on psychometric intelligence:

Do academic-track schools make students smarter? Journal of Educa- tional Psychology, 104, 682– 699. http://dx.doi.org/10.1037/a0027608

Bissig, D., & Lustig, C. (2007). Who benefits from memory training? Psychological Science, 18, 720 –726. http://dx.doi.org/10.1111/j.1467- 9280.2007.01966.x

Bourdieu, P., & Passeron, J.-C. (1977). Reproduction in education, society and culture. Thousand Oaks, CA: Sage.

Brand, J. E., & Xie, Y. (2010). Who benefits most from college? Evidence for negative selection in heterogeneous economic returns to higher education. American Sociological Review, 75, 273–302. http://dx.doi .org/10.1177/0003122410363567

Cawley, J., Heckman, J., & Vytlacil, E. (2001). Three observations on wages and measured cognitive ability. Labour Economics, 8, 419 – 442. http://dx.doi.org/10.1016/S0927-5371(01)00039-2

Child, D. (1969). A comparative study of personality, intelligence and social class in a technological university. British Journal of Educational Psychology, 39, 40 – 46. http://dx.doi.org/10.1111/j.2044-8279.1969 .tb02039.x

Cumming, G. (2014). The new statistics: Why and how. Psychological Science, 25, 7–29. http://dx.doi.org/10.1177/0956797613504966

Deary, I. J., Taylor, M. D., Hart, C. L., Wilson, V., Smith, D. G., Blane, D., & Starr, J. M. (2005). Intergenerational mobility and mid-life status attainment: Influences of childhood intelligence, childhood social fac- tors, and education. Intelligence, 33, 455– 472. http://dx.doi.org/ 10.1016/j.intell.2005.06.003

Donnellan, M. B., Oswald, F. L., Baird, B. M., & Lucas, R. E. (2006). The mini-IPIP scales: Tiny-yet-effective measures of the Big Five factors of personality. Psychological Assessment, 18, 192–203. http://dx.doi.org/ 10.1037/1040-3590.18.2.192

Duckworth, A. L., Weir, D., Tsukayama, E., & Kwok, D. (2012). Who does well in life? Conscientious adults excel in both objective and subjective success. Frontiers in Psychology, 3, 356. http://dx.doi.org/ 10.3389/fpsyg.2012.00356

Duncan, G. J., & Brooks-Gunn, J. (1997). Consequences of growing up poor. New York, NY: Russell Sage Foundation Press.

Duncan, O. D. (1961). A socioeconomic index for all occupations. In A. J. Reiss (Ed.), Occupations and social status (pp. 109 –138). New York, NY: Free Press.

Duncan, O. D., Featherman, D. L., & Duncan, B. (1972). Socioeconomic background and achievement. New York, NY: Seminar Press.

Edwards, J. R. (2002). Alternatives to difference scores: Polynomial re- gression analysis and response surface methodology. In F. Drasgow & N. W. Schmitt (Eds.), Advances in measurement and data analysis (pp. 350 – 400). San Francisco, CA: Jossey-Bass.

Flanagan, J. C., Dailey, J. T., Shaycoft, M. F., Gorham, W. A., Orr, D. B., & Goldberg, I. (1960). Designing the study. Tech. Rep. to the U.S. Office of Education, Cooperative Research Project No. 635. Pittsburgh, PA: Project TALENT Office, University of Pittsburgh.

Galobardes, B., Shaw, M., Lawlor, D. A., Lynch, J. W., & Davey Smith, G. (2006). Indicators of socioeconomic position (Pt. 1). Journal of Epidemiology and Community Health, 60, 7–12. http://dx.doi.org/ 10.1136/jech.2004.023531

Gottfredson, L. S. (2002). Where and why g matters: Not a mystery. Human Performance, 15, 25– 46.

Gustafsson, J.-E., & Undheim, J. O. (1996). Individual differences in cognitive functions. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of educational psychology (pp. 186 –242). New York, NY: Prentice Hall International.

Hauser, R. M., & Palloni, A. (2011). Adolescent IQ and survival in the Wisconsin longitudinal study. The Journals of Gerontology: Series B: Psychological Sciences and Social Sciences, 66(Suppl. 1), i91–i101. http://dx.doi.org/10.1093/geronb/gbr037

T hi

s do

cu m

en t

is co

py ri

gh te

d by

th e

A m

er ic

an P

sy ch

ol og

ic al

A ss

oc ia

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or on

e of

it s

al li

ed pu

bl is

he rs

. T

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ar ti

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is in

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fo r

th e

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of th

e in

di vi

du al

us er

an d

is no

t to

be di

ss em

in at

ed br

oa dl

y.

488 DAMIAN, SU, SHANAHAN, TRAUTWEIN, AND ROBERTS

Hauser, R. M., & Warren, J. (1997). Socioeconomic indexes of occupa- tions: A review, update, and critique. Sociological Methodology, 27, 177–298. http://dx.doi.org/10.1111/1467-9531.271028

Heckman, J. J. (2006). Skill formation and the economics of investing in disadvantaged children. Science, 312, 1900 –1902. http://dx.doi.org/ 10.1126/science.1128898

Heckman, J. J., & Kautz, T. (2012). Hard evidence on soft skills. Labour Economics, 19, 451– 464. http://dx.doi.org/10.1016/j.labeco.2012.05 .014

John, O., Donahue, E. M., & Kentle, R. (1991). The Big Five Inventory. Berkeley, CA: University of California, Berkeley, Institute of Personal- ity and Social Research.

Johnson, W., McGue, M., & Iacono, W. G. (2006). Genetic and environ- mental influences on academic achievement trajectories during adoles- cence. Developmental Psychology, 42, 514 –532.

Johnson, W., McGue, M., & Iacono, W. G. (2007). Socioeconomic status and school grades: Placing their association in broader context in a sample of biological and adoptive families. Intelligence, 35, 526 –541.

Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2004). Academic perfor- mance, career potential, creativity, and job performance: Can one con- struct predict them all? Journal of Personality and Social Psychology, 86, 148 –161.

Major, J. T., Johnson, W., & Deary, I. J. (2014). Linear and nonlinear associations between general intelligence and personality in Project TALENT. Journal of Personality and Social Psychology, 106, 638 – 654. http://dx.doi.org/10.1037/a0035815

Merton, R. K. (1968). The Matthew effect in science: The reward and communication systems of science are considered. Science, 159, 56 – 63. http://dx.doi.org/10.1126/science.159.3810.56

Mirowsky, J., & Ross, C. (2003). Education, social status, and health. New York, NY: Aldine De Gruyter.

Moffitt, T. E., Arseneault, L., Belsky, D., Dickson, N., Hancox, R. J., Harrington, H., . . . Caspi, A. (2011). A gradient of childhood self- control predicts health, wealth, and public safety. PNAS Proceedings of the National Academy of Sciences of the United States of America, 108, 2693–2698. http://dx.doi.org/10.1073/pnas.1010076108

Noftle, E. E., & Robins, R. W. (2007). Personality predictors of academic outcomes: Big five correlates of GPA and SAT scores. Journal of Personality and Social Psychology, 93, 116 –130. http://dx.doi.org/ 10.1037/0022-3514.93.1.116

Poropat, A. E. (2009). A meta-analysis of the five-factor model of person- ality and academic performance. Psychological Bulletin, 135, 322–338. http://dx.doi.org/10.1037/a0014996

Pozzebon, J., Damian, R. I., Hill, P., Lin, Y., Roberts, B., & Lapham, S. (2013). Establishing the validity and reliability of the Project Talent Personality Inventory. Frontiers in Psychology, 4, 968.

Roberts, B. W., Caspi, A., & Moffitt, T. E. (2003). Work experiences and personality development in young adulthood. Journal of Personality and Social Psychology, 84, 582–593. http://dx.doi.org/10.1037/0022-3514 .84.3.582

Roberts, B., Jackson, J. J., Duckworth, A. L., & Von Culin, K. (2011). Personality measurement and assessment in large panel surveys. Forum for Health Economics & Policy, 14, 1–34.

Roberts, B. W., Kuncel, N. R., Shiner, R. L., Caspi, A., & Goldberg, L. R. (2007). The power of personality: The comparative validity of person-

ality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspectives on Psychological Science, 2, 313–345. http://dx.doi.org/10.1111/j.1745-6916.2007.00047.x

Ross, C. E., & Mirowsky, J. (2011). The interaction of personal and parental education on health. Social Science & Medicine, 72, 591–599. http://dx.doi.org/10.1016/j.socscimed.2010.11.028

Shanahan, M. J., Bauldry, S., Roberts, B. W., Macmillan, R., & Russo, R. (2014). Personality and the reproduction of social class. Social Forces, 93, 209 –240. http://dx.doi.org/10.1093/sf/sou050

Shanock, L. R., Baran, B. E., Gentry, W. A., Pattison, S. C., & Heggestad, E. D. (2010). Polynomial regression with response surface analysis: A powerful approach for examining moderation and overcoming limita- tions of difference scores. Journal of Business and Psychology, 25, 543–554. http://dx.doi.org/10.1007/s10869-010-9183-4

Stevens, G., & Featherman, D. L. (1981). A revised socioeconomic index of occupational status. Social Science Research, 10, 364 –395. http://dx .doi.org/10.1016/0049-089X(81)90011-9

Su, R. (2012). The power of vocational interests and interest congruence in predicting career success (Unpublished doctoral dissertation). Uni- versity of Illinois at Urbana-Champaign, Champaign, IL.

Sutin, A. R., Costa, P. T., Jr., Miech, R., & Eaton, W. W. (2009). Personality and career success: Concurrent and longitudinal relations. European Journal of Personality, 23, 71– 84. http://dx.doi.org/10.1002/ per.704

Tharenou, P. (1997). Managerial career advancement. In C. L. Cooper & I. T. Robertson (Eds.), International review of industrial and organiza- tional psychology (pp. 40 –93). New York, NY: Wiley.

Top 10 George Carlin quotes. (2008, November 10). Time. Retrieved from http://content.time.com/time/specials/packages/article/0,28804, 1858074_1858085_1858083,00.html

U.S. Bureau of Census. (1971). Classified index of industries and occupa- tions: 1970 census of population. Washington, DC: U. S. Government Printing Office.

Verhaeghen, P., & Marcoen, A. (1996). On the mechanisms of plasticity in young and older adults after instruction in the method of loci: Evidence for an amplification model. Psychology and Aging, 11, 164 –178. http:// dx.doi.org/10.1037/0882-7974.11.1.164

Verhaeghen, P., Marcoen, A., & Goossens, L. (1992). Improving memory performance in the aged through mnemonic training: A meta-analytic study. Psychology and Aging, 7, 242–251. http://dx.doi.org/10.1037/ 0882-7974.7.2.242

Wai, J., Lubinski, D., & Benbow, C. (2009). Spatial ability for STEM domains: Aligning over 50 years of cumulative psychological knowl- edge solidifies its importance. Journal of Educational Psychology, 101, 817– 835. http://dx.doi.org/10.1037/a0016127

Walberg, H. J., & Tsai, S. L. (1983). Matthew effects in education. American Educational Research Journal, 20, 359 –373.

Wise, L., McLaughlin, D., & Steel, L. (1979). The Project TALENT Data Bank Handbook, Revised. Palo Alto, CA: American Institutes for Re- search.

Received June 4, 2014 Revision received September 9, 2014

Accepted September 10, 2014 �

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489PERSONALITY, SOCIAL BACKGROUND, AND ATTAINMENT