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Student Characteristics and Behaviors at Age 12 Predict Occupational Success 40 Years Later Over and Above Childhood IQ and Parental Socioeconomic Status

July 2015
Developmental Psychology 51(9)

DOI:10.1037/dev0000025

Source
PubMed

Authors:

Marion Spengler

Medical School Berlin

Martin Brunner

Universität Potsdam

Rodica Ioana Damian

University of Houston

Oliver Lüdtke

IPN - Leibniz Institute for Science and Mathematics Education

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Drawing on a 2-wave longitudinal sample spanning 40 years from childhood (age 12) to middle adulthood (age 52), the present study was designed to examine how student characteristics and behaviors in late childhood (assessed in Wave 1 in 1968) predict career success in adulthood (assessed in Wave 2 in 2008). We examined the influence of parental socioeconomic status (SES), childhood intelligence, and student characteristics and behaviors (inattentiveness, school entitlement, responsible student, sense of inferiority, impatience, pessimism, rule breaking and defiance of parental authority, and teacher-rated studiousness) on 2 important real-life outcomes (i.e., occupational success and income). The longitudinal sample consisted of N = 745 persons who participated in 1968 (M = 11.9 years, SD = 0.6; 49.9% female) and 2008 (M = 51.8 years, SD = 0.6; 53.3% female). Regression analyses and path analyses were conducted to evaluate the direct and indirect effects (via education) of the predictors on career success. The results revealed direct and indirect influences of student characteristics (responsible student, rule breaking and defiance of parental authority, and teacher-rated studiousness) across the life span on career success after adjusting for differences in parental SES and IQ at age 12. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

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Student Characteristics and Behaviors at Age 12 Predict Occupational

Success 40 Years Later Over and Above Childhood IQ and Parental

Socioeconomic Status

Marion Spengler

University of Luxembourg

Martin Brunner

Free University and Berlin-Brandenburg Institute for

School Quality

Rodica I. Damian

University of Illinois at Urbana-Champaign

Oliver Lüdtke

Leibniz Institute for Science and Mathematics Education

Romain Martin

University of Luxembourg

Brent W. Roberts

University of Illinois at Urbana-Champaign

Drawing on a 2-wave longitudinal sample spanning 40 years from childhood (age 12) to middle

adulthood (age 52), the present study was designed to examine how student characteristics and behaviors

in late childhood (assessed in Wave 1 in 1968) predict career success in adulthood (assessed in Wave 2

in 2008). We examined the influence of parental socioeconomic status (SES), childhood intelligence, and

student characteristics and behaviors (inattentiveness, school entitlement, responsible student, sense of

inferiority, impatience, pessimism, rule breaking and defiance of parental authority, and teacher-rated

studiousness) on 2 important real-life outcomes (i.e., occupational success and income). The longitudinal

sample consisted of N⫽745 persons who participated in 1968 (M⫽11.9 years, SD ⫽0.6; 49.9%

female) and 2008 (M⫽51.8 years, SD ⫽0.6; 53.3% female). Regression analyses and path analyses

were conducted to evaluate the direct and indirect effects (via education) of the predictors on career

success. The results revealed direct and indirect influences of student characteristics (responsible student,

rule breaking and defiance of parental authority, and teacher-rated studiousness) across the life span on

career success after adjusting for differences in parental SES and IQ at age 12.

Keywords: student characteristics and behaviors, childhood personality, teacher rating, longitudinal

MAGRIP study, career success and income

Supplemental materials: http://dx.doi.org/10.1037/dev0000025.supp

Parents and teachers spend countless hours discussing, worrying

about, and working on how their children approach schoolwork.

Students themselves often share the same concerns and thoughts

about how their actions at school might affect their future. Are any

of these concerns well founded? Obviously, one can make the case

that how students perform in school, at least in terms of their

overall grades, will have some concrete ramifications for their near

future, such as whether they get into college. However, what about

other qualities that students have? Does it matter if they have a

good attitude in school? Does it matter whether they work hard as

a student or suffer from debilitating anxiety when faced with tests

and challenges? Recently, scientists have argued that educational

systems should pay more attention to the soft skills (or social-

emotional skills) students learn, under the assumption that these

skills might help them succeed later in life (Tough, 2012). Unfor-

tunately, there have been very few opportunities to investigate

Marion Spengler, Research Center for Educational Measurement

and Applied Cognitive Science, University of Luxembourg; Martin

Brunner, Free University and Berlin-Brandenburg Institute for School

Quality; Rodica I. Damian, Department of Psychology, University of

Illinois at Urbana-Champaign; Oliver Lüdtke, Leibniz Institute for

Science and Mathematics Education, Christian-Albrechts-University

Kiel; Romain Martin, Research Center for Educational Measurement

and Applied Cognitive Science, University of Luxembourg; Brent W.

Roberts, Department of Psychology, University of Illinois at Urbana-

Champaign.

Romain Martin is now at the Luxembourg Centre for Educational

Testing, University of Luxembourg.

This study was supported by a grant from the Luxembourgish Fonds

National de la Recherche (PELEDU–Personality in Learning and Educa-

tion, project no. C11/LM/1168993).

Correspondence concerning this article should be addressed to Marion

Spengler, who is now at the Faculty of Language and Literature, Human-

ities, Arts and Education, Research Unit for Education, Culture, Cognition,

University of Luxembourg, Campus Belval, Maison des Sciences Hu-

maines, 11, Porte des Sciences, 4366 Esch/Alzette, Luxembourg. E-mail:

marion.spengler@gmail.com

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2015, Vol. 51, No. 8, 000 0012-1649/15/$12.00 http://dx.doi.org/10.1037/dev0000025

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whether students’ behaviors and thoughts about school have any

lasting effects on their lifetime accomplishments.

In the current study, we used a unique data set to address these

questions. Specifically, we used data from the Luxembourgish

MAGRIP study. The original sample included more than 2,800

students from the 6th grade, representing half of the student

population of this age in Luxembourg in 1968 at the time of the

assessment. The initial assessment took place in primary school

before the selection process had begun to direct students into

different school tracks. It included measures of intelligence, edu-

cational aspirations, socioeconomic status (SES), family back-

ground, and a questionnaire on students’ school-related and every-

day feelings, thoughts, and habits. The items covered either

school-related (e.g., “If I am interrupted while doing my home-

work, I still try hard to do it properly”) or nonschool-related

contexts (e.g., “Life is usually hard; only sometimes is it nice”).

Moreover, the teachers were asked to rate their students on studi-

ousness because teachers tend to be a valid source of information

with regard to studiousness and willingness to learn. Recently, the

MAGRIP sample was tracked down and followed up, and re-

searchers collected basic information on the targets’ lifetime edu-

cational and occupational achievements. Thus, we had a unique

opportunity to ask about the extent to which it really matters how

someone acted and thought in school. To evaluate the relevance of

such constructs across the life span, we related them to important

later life outcomes, namely, status and occupational achievement

while controlling for other important childhood information (IQ

and parental SES). The associations between childhood IQ, paren-

tal SES, and income that are present in the MAGRIP sample have

been previously reported (Fischbach, Baudson, Preckel, Martin, &

Brunner, 2013). These analyses were conducted before the data on

the childhood characteristics had been analyzed. The present arti-

cle is the first investigation to use the childhood student charac-

teristics and the teachers’ ratings of studiousness.

Theoretical Models of Status Attainment

To better understand the effects of student characteristics and

behaviors on status attainment, as well as their interplay with other

predictors and their relations to life outcomes, we considered

different theoretical perspectives, which can be split into (a) soci-

ological and (b) psychological approaches.

In sociological approaches, the main focus has been on the

effects of parental SES on students’ academic achievement, edu-

cational attainment, and occupational success. For example,

Boudon (1974) introduced the distinction between primary and

secondary influences of family SES to explain differences in

educational outcomes. In his model, primary influences are those

factors that affect educational attainment indirectly, through their

impact on educational competences, whereas secondary influences

are those factors that have a direct impact on educational attain-

ment.

Moreover, sociological theories of status attainment also include

human resources (i.e., capital) that play a role in addition to social

resources such as SES (see Coleman, 1988). Most of these theories

can be traced back to Blau and Duncan (1967) who assumed that

after controlling for direct and indirect influences of parental SES,

the most important form of human capital was educational

achievement (and prior occupational status). Although most of

these theoretical models focus on social capital (e.g., SES) as a

predictor of higher occupational attainment, they tend to provide

only a limited inclusion of psychological factors that might explain

status and occupational attainment.

Psychological approaches aim at adding individual difference

variables, such as general cognitive ability, personality, and mo-

tivational variables, to the prediction of occupational outcomes,

and testing their incremental validity above and beyond SES (e.g.,

Damian et al., 2014). We will present three models that provide a

broad and integrative view on the relation of the different sets of

predictors: (a) the Eccles expectancy-value model (see Eccles,

Wigfield, & Schiefele, 1998), (b) the Wisconsin model (Sewell,

Haller, & Portes, 1969), and (c) the Credé and Kuncel model of

academic performance (Credé & Kuncel, 2008).

The Eccles expectancy-value model is a broad model that

links psychological and social factors to predict educational,

vocational, and other achievement-related choices and activi-

ties. The Eccles model is one of the most comprehensive

models and includes a broad range of factors, such as cultural

norms, experiences, aptitudes, socializers’ influences, personal

expectations, beliefs, and attitudes in the prediction of expec-

tations (Eccles, 2005). Although the focus of this model is to

understand the development of expectations (such as self-

concept and task value; e.g., Eccles & Wigfield, 2002), the

model highlights general pathways that might help identify

possible predictors of educational and occupational success.

The model has two basic components, a psychological and a

socialization component: Expectations and beliefs (psycholog-

ical factors) as well as beliefs and behaviors of parents, teach-

ers, and peers, and parental SES (social factors).

The Wisconsin model of status attainment is a psychological

model of how parental SES is linked to occupational outcomes via

educational attainment, aspirations, academic performance, gen-

eral cognitive ability, and the role of significant others (Sewell et

al., 1969). One critical feature of the Wisconsin model of status

attainment is the assumption that educational attainment, aspira-

tions, and general cognitive ability have direct effects on later

occupational status. However, the Wisconsin model omits other

psychological dimensions such as personality traits or student

characteristics, behaviors, and attitudes.

In a more recent approach, Credé and Kuncel (2008) pro-

posed an academic performance model that builds on the Wis-

consin status attainment model by incorporating a variety of

predictors of performance. They introduced student character-

istics as important predictors of academic success in addition to

general cognitive ability, prior and actual knowledge, person-

ality, and motivation. Moreover, they showed that student char-

acteristics could be subsumed under three broad domains: study

habits, skills, and attitudes. The Credé and Kuncel model,

although more inclusive with regard to possible predictors, is

not as encompassing in its scope with regard to the outcomes,

such as educational and occupational attainment. Although the

Credé and Kuncel model did not include occupational attain-

ment as an outcome, it is reasonable to assume that the predic-

tors that are relevant for educational attainment would also be

relevant for occupational attainment, given that education is one

of the strongest predictors of career success.

Each of these theories provides different sets of predictors and

focuses on different developmental periods and outcomes of the

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process. The sociological and Eccles models both focus on edu-

cational attainment as a primary outcome and stepping stone for

later occupational attainment. The Credé and Kuncel model, de-

spite being focused primarily on children and teens, may be

equally applicable to adults. Based on these models, we would

assume that family background and cognitive predictors should

have their largest effect on early adulthood educational attainment.

Less is known about the long-term predictors of occupational

success, but prior work in personality psychology has focused on

personality traits as potential predictors of success throughout

adulthood (e.g., Roberts et al., 2007). Moreover, psychological

models, like Credé and Kuncel’s hold that both personality and

intelligence are largely independent of family background in

their effects on occupational outcomes. Therefore, we expect

personality-related variables and cognitive ability to play multiple

roles in both educational attainment, which occurs primarily in

young adulthood, and in occupational attainment across the life

course. Inspired by the different theoretical approaches we adapted

a comprehensive set of factors that should predict outcomes both

in the short and long run.

Still, little is known about the mechanisms through which job

performance and career success are determined by cognitive (e.g.,

intelligence) and noncognitive predictors (e.g., student character-

istics) as well as socioeconomic family background. Credé and

Kuncel (2008) as well as Sewell and colleagues (1969) assumed

indirect paths in their models. Especially in a longitudinal study,

possible mediating variables might be able to explain the mecha-

nisms in a little more detail, most prominently educational attain-

ment (see Shanahan, Hill, Roberts, Eccles, & Friedman, 2014).

Ending up with a good job or high income depends on successful

attainment of the previous step. Educational attainment, which is

an important determinant for entering the job market, can there-

fore, be seen as the most important gateway into the job market.

This can be understood as a process of cumulative advantage (see

DiPrete & Eirich, 2006): having good education leads to better

jobs. Therefore, it seems reasonable to treat educational attainment

as a mediator. We know from previous research that SES, general

cognitive ability, and personality predict academic success and

educational attainment (see Poropat, 2009;Spengler, Lüdtke, Mar-

tin, & Brunner, 2013). Accordingly, in addition to the direct

influences of SES, general cognitive ability, and student charac-

teristics on life outcomes, we might expect an indirect pathway for

each of our predictors via education. Therefore, it is important to

use a multidetermined approach to explain career success.

Longitudinal Studies of Career Success

The goal of uncovering the long-term predictors of career suc-

cess is highly relevant to several academic fields, such as psychol-

ogy, education, sociology, and economics (see Almlund, Duck-

worth, Heckman, & Kautz, 2011;Blossfeld, Ro␤bach, & von

Maurice, 2011;Heckman, 2006). Previous studies investigating

predictors of occupational success, as measured by high SES or a

good income, have focused on cognitive predictors such as intel-

ligence (Gottfredson, 2002;Heckman, 2006;Kuncel, Hezlett, &

Ones, 2004;Schmidt & Hunter, 2004). Other studies have focused

on the roles of SES and prestige in predicting future achievement

and occupational success (Bradley & Corwyn, 2002;Caro, Cor-

tina, & Eccles, 2014;Heckman, Stixrud, & Urzua, 2006;Schnabel,

Alfeld, Eccles, Köller, & Baumert, 2002). Finally, some studies

have focused on the role of education as a predictor of career

success (e.g., Heckman, 2006).

In recent decades, researchers have investigated a broad array of

determinants of academic and educational success. Such determi-

nants can also be used as a basis for the identification of predictors

of career success because most variables probably overlap between

the two outcomes. Furthermore, educational success is one of the

most important predictors of career success.

To understand the determinants of career success, it is essential

to conduct longitudinal investigations of the relations between

individual differences and occupational outcomes. As the main

focus of the current investigation was to elaborate on the influ-

ences of childhood characteristics on later occupational success,

we focused the literature review on prospective longitudinal stud-

ies that included noncognitive predictors measured early in life.

Viinikainen, Kokko, Pulkkinen, and Pehkonen (2010) drew on

the Jyväskylä Longitudinal Study of Personality and Social De-

velopment (JYLS; N⫽243) in which four dimensions of child-

hood characteristics were assessed via teacher ratings and peer

nominations: Extraversion, inattentiveness, aggression, and con-

structiveness. Constructiveness (active and well-controlled social

behavior) assessed at age 8 had a positive association with income

at age 43 (a 1 SD increase in constructiveness was related to a 10%

increase in income). Those results remained even after controlling

for education, sex, and work-related information.

Similarly, childhood personality traits were shown to predict

academic attainment and work competence (Shiner, Masten, &

Roberts, 2003). In a sample of children between the ages of 8 and

12 (N⫽205), academic conscientiousness, agreeableness, sur-

gency, and mastery motivation were assessed by a combination of

teacher reports, self-reports, and parent reports. After controlling

for gender, academic competence, and childhood IQ, academic

conscientiousness and agreeableness still had small but significant

effects on academic attainment and work competence 20 years

later.

Judge and colleagues (Judge, Higgins, Thoresen, & Barrick,

1999) related child and adult personality to intrinsic and extrinsic

career success in middle age (N⫽116 to 118). Adolescent

personality ratings were made at ages 12 to 14 and 15 to 18 and

were based on different resources (observational data; interviews

with participants, teacher, and parents). Childhood Neuroticism

was found to be negatively related and childhood Extraversion to

be positively related to extrinsic career success (occupational sta-

tus and income). Conscientiousness was associated with both

intrinsic and extrinsic outcomes. The personality factors predicted

career success over and above general cognitive abilities.

Recently, and relevant to the focus on school behaviors, Lleras

(2008) investigated the contribution of noncognitive factors in

predicting educational attainment and income. Cognitive abilities,

work habits, social skills and behaviors, and family background

were assessed in a cohort of 10th graders. In predicting income 10

years later, work habits, social skills, and participation in extra-

curricular activities predicted educational attainment and income

after controlling for general cognitive abilities (N⫽7,656).

In summary, existing research has indicated that childhood

characteristics should be related to adult achievements, though few

studies have focused on school-related behaviors. Furthermore, no

research to date has linked school-related behaviors to educational

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and occupational outcomes across a long period of the adult life

course such as the one we had available in the MAGRIP study.

Moreover, most studies have relied on only small or moderate

sample sizes.

The Present Investigation

Because MAGRIP used a unique set of questions to assess

student characteristics and behaviors in adolescence, and because

no documentation of these measures was archived, the assessment

framework that was behind the student questionnaire from 1968

had to be reconstructed. Therefore, as a first step, we examined

both the factor structure of the item set in the original MAGRIP

sample as well as in a sample of students assessed recently. This

initial step provides critical information about the meaning of the

measures administered at that time. We first identified the structure

underlying the survey items, we then examined the nomological

network of correlations linked to these student characteristics and

behaviors in an attempt to better understand how they might be

related to later educational and occupational outcomes. Detailed

information about the development and validity of the scales can

be found in the online Supplementary Material.

In the second step, we used the dimensions identified in the first

step to predict educational attainment, occupational attainment,

and income several decades after the original assessment of stu-

dent characteristics. In this latter step, we included variables com-

mon to the occupational attainment models described above, such

as cognitive ability and childhood SES.

The comprehensive set of predictors allowed us to test mul-

tiple factors drawn from sociological, educational, and psycho-

logical models reviewed above. For example, we used family

background (SES) as a primary predictor, which is consistent

with sociological models. We also included psychological vari-

ables that were consistent with both personality and educational

models, such as the Eccles model. We also included teacher

ratings of studiousness, which provides an approximation of

Eccles “social factors.”

On the basis of the results from previous longitudinal studies as

outlined above, we expected childhood IQ, parental SES, educa-

tional attainment, and the student characteristics and behaviors to

independently predict occupational success and income. Also con-

sistent with prior work predicting occupational attainment, we

tested educational attainment as a potential mediator of childhood

factors when predicting adult work outcomes. Based on the theo-

retical models we would expect that childhood IQ and parental

SES will have both significant direct and significant indirect paths.

Concerning our student characteristics, this is the first study that

investigated direct and indirect paths in one model. Therefore, we

have no explicit expectations about the significance of the direct

and indirect paths.

Method

Participants

The present investigation capitalized on longitudinal data from

the Luxembourgish MAGRIP study (Brunner & Martin, 2011),

which used a prospective epidemiological cohort design spanning

40 years, from 1968 (Wave 1) to 2008 (Wave 2). The multistage

sampling procedure is described in detail in the online Supplemen-

tary Material (Figure 1). A random stratified representative sub-

sample, which was stratified by region of residence in 1968 and

gender, from Wave 1 participated in the second wave (N⫽745;

M⫽51.8 years of age, SD ⫽0.6; 53.3% female) in 2008. In 1968,

data on student characteristics, cognitive ability, and family back-

ground were collected from about half of the Luxembourgish

student population at the end of primary school when most chil-

dren were in the 6th grade (N⫽2,824; M⫽11.9 years of age,

SD ⫽0.6; 49.9% female). In Grade 6 students were still in primary

school. The selection process into different secondary school

tracks took place after Grade 6 in Luxembourg.

Analyses were computed on data from the participants for

whom complete data for both waves were available. We excluded

participants when they had missing values on the outcome vari-

ables. This resulted in a final sample of N⫽730 participants for

the analyses on occupational success and N⫽575 participants for

the analyses on income.

Analyses concerning selection bias showed that the sample at

Wave 2 was fairly representative of the original sample. Relative

to the total 1968 sample, participants had slightly higher mean

childhood general cognitive ability (Cohen’s d⫽0.20) and child-

hood SES (d⫽0.08). With respect to the seven scales of the

MAGRIP personality scales (MPS), the four largest mean differ-

ences relative to the total 1968 sample were observed for pessi-

mism (d⫽⫺0.15), sense of inferiority (d⫽⫺0.11), school

entitlement (d⫽⫺0.08), and the responsible student scale (d⫽

0.08) with negative values indicating that persons participating in

both waves of measurement had lower levels on these scales.

Although statistically significant, the magnitudes of the differences

were quite small.

Measures

Childhood student characteristics and behaviors. Students

completed the MPS, which is a questionnaire that includes a large

set of items concerning the students’ feelings, thoughts, and habits

toward their school and everyday lives. The questionnaire consists

of 108 items with a dichotomous answer format for which students

have to decide whether the item is true for them or not. We divided

the sample into school- and nonschool-related items and conducted

two sets of exploratory factor analyses (EFA) with oblique rota-

tions for each set of variables. Four school-related and three

nonschool-related scales were extracted: inattentiveness, school

entitlement, responsible student, sense of inferiority, impatience,

pessimism, and rule breaking and defiance of parental authority.

The validation of the scales and the items can be found in detail in

the online Supplementary Material.

School entitlement describes the demands that students make

of their teachers or their expectations of school in general (e.g.,

“Students’ wishes should always be fulfilled”). The responsible

student can be summarized as industrious and achievement-

striving (e.g., “I usually try hard to do my homework very

accurately and carefully”). The sense of inferiority scale in-

cludes items such as “If an exercise is very difficult, I give up

more easily than my classmates,” and depicts the students’

(mainly upward) social comparisons in school contexts. Impa-

tience covers behaviors related to impatience (e.g., “I run out of

patience quickly”). Pessimism includes items that describe stu-

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dents’ negative and depressed view of the world (e.g., “I am

usually sad; I am happy only sometimes”). Rule breaking and

defiance of parental authority encompasses items that demon-

strate a low level of rule orientation (e.g., “I have talked back

to my mother before”).

Childhood studiousness (teacher rating). Teachers’ ratings

of students’ studiousness at age 12 were assessed by a single item

on which teachers rated their students according to the students’

studiousness. Teachers responded using a 5-point rating scale (1 ⫽

very low,2⫽low,3⫽average,4⫽high, and 5 ⫽very high).

Childhood intelligence. In 1968, intelligence was assessed by

14 subtests of the “Leistungsprüfsystem” (L-P-S [Performance test

system]; Horn, 1962,1983). The L-P-S is a standardized German

intelligence test battery that builds on Thurstone’s (1938) model of

primary mental abilities. Its total score correlates .94 with the total

score of the German version of the Wechsler Adult Intelligence

Scale (Sturm & Büssing, 1982). Each subtest contains 40 items

and has to be completed within strict time constraints as specified

in the test manual. The L-P-S subtests were averaged to create a

single IQ score, which was z-standardized (␣⫽.86).

Parental SES in childhood. Parental SES was assessed by

obtaining children’s descriptions of their parents’ occupations.

These occupations were mapped onto the categories of the Inter-

national Standard Classification of Occupations (ISCO-88; Elias,

1997). For the present study, occupations were transformed using

the International Socio-Economic Index of occupational status

(ISEI; Ganzeboom, De Graaf, & Treiman, 1992). The ISEIs the-

oretical range spans from 16 (farm hands, laborers, helpers, and

cleaners) to 90 (judges), with higher ISEI values indicating higher

SES. In the present study, the highest ISEI value in a family

(usually the father’s ISEI value) was used. Interrater reliability of

this ISEI coding was tested for two independent groups of raters

and was satisfactory (.72).

Educational attainment. Educational attainment was opera-

tionalized as the number of school years attended after Grade 6. To

make sure that the variable reflected actual exposure to educational

opportunities rather than time spent in a classroom, repeated

grades were counted only once.

Occupational success. Participants’ current or most recent

occupation (when participants were unemployment at follow-up)

served as a measure of occupational success. Occupations were

again mapped onto the ISCO code and then transformed into the

ISEI (Ganzeboom et al., 1992). Higher values indicate higher

occupational prestige and SES.

Income. Individual income was assessed with a scale ranging

from less than 150 Euro to 10,000 Euro or more per month.

Participants were presented with 14 income ranges and had to

indicate which range indicated their individual income per month.

For the present analyses, each range was recoded to the midpoint

of the range. For instance, if participants indicated that their

income was between 1,000 Euro and 1,499 Euro, their income was

recoded to 1,225 Euro. For analyses that involved income, we

included only participants with a valid individual income ⬎0 Euro.

Mean income was 5,814 Euro per month for men and 5,168 Euro

per month for women. Therefore, individual income was centered

on the mean for males or females to account for mean differences

between the two sexes before the analyses.

Analyses

To provide a full picture of the relations between childhood

characteristics, educational attainment, and occupational success,

we specified several sets of regression models and two saturated

path models that included mediation via educational attainment.

We began our statistical analyses by investigating a set of

regression models to test the incremental validity of the MPS

scales and the teachers’ ratings of studiousness on occupational

status and income, respectively. To this end, Model Set A included

only IQ and SES, Model Set B additionally included years of

education (over and above Model Set A), and Model Set C

additionally included the MPS scales and the teachers’ ratings of

studiousness (over and above Model Set B).

Further, we investigated two path models that included all of the

predictors from the regression analyses (i.e., the predictor variables

included in regression Model Set C) and occupational status and

income as central outcome variables, respectively. In addition, we

specified years of education as a mediator between all other

predictors and the outcome variable to test whether educational

attainment mediated the effects of our predictors on career success.

Therefore, every predictor (MPS scales, teacher ratings, IQ, and

SES) was allowed to have both a direct and an indirect (via years

of education) path to the outcome variables. Parameters in both

model sets were estimated using Mplus (Muthén & Muthén, 1998-

2012). The percentage of missing data that were included in the

two path models was less than 5% for each variable, respectively.

Hence, missing data were unlikely to be a critical problem. We

used the full information maximum likelihood approach (maxi-

mum likelihood estimation with robust SEs; MLR) to obtain pa-

rameter estimates and SEs that accounted for the missing data (and

that were also robust to the nonnormality of the data).

To evaluate the direct and indirect effects of the predictors, we

ran a mediation (path) analysis. In such a model, the total effect of

a predictor variable on an outcome variable can be decomposed

into a direct and an indirect or mediated effect. The latter effect

represents an effect that is transmitted via one (or more) media-

tor(s). We computed bias-corrected bootstrap confidence intervals

for the model parameter estimates to assess the significance of the

direct, indirect, and total effects of the predictors on the outcomes.

This method accounts for the expected nonnormality of the sam-

pling distribution of the mediation effect (MacKinnon, Lockwood,

Hoffman, West, & Sheets, 2002). In addition, we computed the

proportion of the total effect of the three predictors that were

mediated by educational attainment as a measure of effect size (cf.

Kelley & Preacher, 2012).

For industriousness, we were able to provide a multimethod

approach that included the self- and teacher reports. Both modal-

ities were thought to have the potential to predict the long-term

outcomes.

Results

First, we tested whether students’ characteristics and behaviors

were related to career outcomes. Table 1 shows the means, SDs,

and correlations of the constructs under investigation. School

entitlement, sense of inferiority, and pessimism measured in child-

hood were significantly negatively correlated with the outcome

variables 40 years later (education, occupational success, and

individual income). The responsible student scale measured in

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childhood was significantly positively correlated with occupa-

tional success and individual income. Rule breaking and defiance

of parental authority was positively correlated with income only.

Specifically, the more responsible and industrious students re-

ported finding more prestigious jobs and earning higher incomes

irrespective of whether childhood characteristics and behaviors

were measured by self-report (occupational success: r⫽.16;

income: r⫽.12) or teacher reports (occupational success: r⫽.40;

income: r⫽.29). Moreover, teacher-rated studiousness was pos-

itively related to educational attainment (education: r⫽.41).

School entitlement, sense of inferiority, and pessimism were neg-

atively related to educational attainment (r⫽⫺.21 to r⫽⫺.26),

occupational success (r⫽⫺.21 to r⫽⫺.27), and income

(r⫽⫺.15 to r⫽⫺.17). Moreover, childhood intelligence was

also significantly related to the outcomes (educational attainment:

r⫽.39; occupational success: r⫽.37; income: r⫽.34). Simi-

larly, parental SES was significantly correlated with the outcomes

(educational attainment: r⫽.41; occupational success: r⫽.33;

income: r⫽.21). Furthermore, educational attainment was signif-

icantly associated with occupational success (r⫽.56) and income

(r⫽.47). These correlations provided the basis for the subsequent

analyses. Overall, the correlational pattern showed that most of the

student scales were related to educational attainment, occupational

success, and income.

Second, we tested the incremental validity of each MPS scale

and teachers’ ratings over and above IQ, SES, and educational

attainment by computing regression analyses that had occupational

success and income as outcome variables. We included all scales

that showed significant bivariate correlations with the outcome

variables (see Table 1). The results of these analyses are reported

in Table 2. We found different patterns of results for occupational

success and income (as indicated by the bivariate correlations). To

contrast the influence of the student characteristics and IQ, paren-

tal SES, and educational attainment, we tested three model sets

(see Table 2). In Model Set A, IQ and SES explained 18% of the

variance in occupational status and 10% of the variance in income.

This result was comparable with the single effects of only the

student characteristics as predictors (that were R

⫽.19 and R

⫽

.12, respectively; not displayed in Table 2). Adding educational

attainment in Model Set B increased the explained variance (to

35% and 22%, respectively). When the student scales and teacher-

rated studiousness were added to the regression model (see Model

Set C), all predictors explained 38% of the variance in occupa-

tional status and 27% of the variance in income. In Model Set C

the responsible student scale (␤⫽.08), teacher-rated studiousness

(␤⫽.13), IQ (␤⫽.10), parental SES (␤⫽.07), and years of

education (␤⫽.42) were significant predictors of occupational

success. Income was predicted by rule breaking and defiance of

parental authority (␤⫽.12), IQ (␤⫽.15), and years of education

(␤⫽.37). To investigate the possible suppression effects of any

predictor that was not significantly related to the outcomes in the

bivariate correlations we reran Model C with all possible predic-

tors. The results did not differ from the results presented in Table

2and are, therefore, not included in the article (when predicting

occupational status, the responsible student scale (␤⫽.09),

teacher-rated studiousness (␤⫽.12), IQ (␤⫽.09), parental SES

(␤⫽.07), and years of education (␤⫽.42) were significant

predictors. Income was predicted by rule breaking and defiance of

parental authority (␤⫽.12), IQ (␤⫽.16), and years of education

(␤⫽.38). In summary, we demonstrated that the responsible

student scale, teacher-rated studiousness, and rule breaking and

defiance of parental authority showed incremental validity over

and above IQ, parental SES, and educational attainment.

We also tested possible moderation effects of IQ and the student

characteristics as well as SES and the student characteristics. None

of the moderations was significant and we, therefore, decided to

not present the results in the manuscript.

Third, we tested whether educational attainment would mediate

the effects of student characteristics and behaviors, intelligence,

and parental SES on occupational success. Therefore, we tested

two path models, one for each outcome variable. The results of

these analyses are reported in Table 3, which contains the stan-

dardized coefficients for each path and the explained variance in

the outcome variables. As we were interested in the direct and

indirect effects of childhood personality characteristics, childhood

IQ, and parental SES, we tested a mediation model in which

educational attainment (operationalized as years of education)

served as a mediator.

The results obtained for Model 1 (see Table 3) showed that rule

breaking and defiance of parental authority, sense of inferiority,

Table 1

Descriptive Statistics for and Bivariate Correlations Between Study Variables

MSD␣2345678910111213

1. Inattentiveness .64 .29 .77 .26 ⴚ.22 .31 .60 .16 .65 .03 .05 ⫺.04 .00 .03 .06

2. School entitlement .39 .23 .71 ⴚ.17 .40 .33 .54 .12 ⴚ.28 ⴚ.22 ⴚ.18 ⴚ.21 ⴚ.21 ⴚ.15

3. Responsible student .83 .14 .57 ⴚ.18 ⴚ.16 ⫺.03 ⴚ.16 .18 .13 .03 .10 .16 .12

4. Sense of inferiority .44 .25 .60 .41 .50 .19 ⴚ.35 ⴚ.28 ⴚ.15 ⴚ.26 ⴚ.27 ⴚ.17

5. Impatience .51 .35 .51 .31 .37 ⫺.08 ⫺.07 ⫺.07 ⫺.06 ⫺.06 ⫺.06

6. Pessimism .39 .24 .67 .06 ⴚ.34 ⴚ.30 ⴚ.17 ⴚ.26 ⴚ.26 ⴚ.17

7. Rule breaking and defiance .56 .35 .68 .13 .08 .00 .08 .07 .14

8. Studiousness (teacher ratings) 3.6 1.01 — .41 .28 .41 .40 .29

9. IQ .00 1.00 — .24 .39 .37 .34

10. Parental SES 40.14 12.65 — .41 .33 .21

11. Years of education 5.83 3.46 — .56 .47

12. Occupational status 46.52 14.59 — .55

13. Individual income 3,134.65 2,337.09 —

Note. SES ⫽socioeconomic status (at Time 1); occupational status was coded as the International Socioeconomic Index of Occupational Status.

Individual income was mean-centered for males and females at age 52. Characters in bold indicate significant correlations (p⬍.01).

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teacher-rated studiousness, IQ, and parental SES were signifi-

cantly related to educational attainment and explained 29% of its

variance. For occupational success, the predictors that were sig-

nificant in the regression analyses were also significant here (re-

sponsible student scale, teacher-rated studiousness, IQ, parental

SES, and years of education). In summary, the predictor variables

explained 38% of the variance in occupational success after 40

years.

In predicting income, the pattern for the personality character-

istics was slightly different (see Model 2). Sense of inferiority,

teacher-rated studiousness, intelligence, and parental SES were

significantly related to educational attainment and explained 32%

of its variance. For individual income, the predictors that were

significant in the regression analyses were also significant here

(defiance of parental authority, intelligence, and years of educa-

tion). In summary, the predictor variables explained 28% of the

variance in individual income after 40 years.

The results of the mediation analyses are reported in Table 4,

which comprises the standardized direct, indirect, and total effects

of the predictors (via educational attainment) on the outcomes

(occupational success and individual income). We did not display

the decomposition of the effects of school entitlement and pessi-

Table 2

Results of Linear Regression Analysis: Predictors of Career Success

Model Set A Model Set B Model Set C

␤95% CI ␤95% CI ␤95% CI

Occupational status (ISEI)

IQ .31 [.25; .37] .16 [.10; .22] .10 [.03, .16]

Parental SES .23 [.16; .29] .08 [.03; .15] .07 [.01, .13]

Years of education .47 [.41; .54] .42 [.35, .49]

School entitlement .00 [⫺.07, .08]

Responsible student .08 [.01, .14]

Sense of inferiority ⫺.04 [⫺.11, 03]

Pessimism ⫺.05 [⫺.12, .03]

Studiousness (teacher ratings) .13 [.06, .20]

.18 .35 .38

Individual income

IQ .26 [.19; .33] .13 [.06; .20] .15 [.07, .24]

Parental SES .12 [.04; .20] .00 [⫺.07, .08] .00 [⫺.09, .07]

Years of education .41 [.31; .50] .37 [.27, .46]

School entitlement ⫺.02 [⫺.11, .08]

Responsible student .05 [⫺.03, .12]

Sense of inferiority ⫺.01 [⫺.10, .08]

Pessimism ⫺.02 [⫺.11, .08]

Rule breaking and defiance .12 [.04, .19]

Studiousness (teacher ratings) .03 [⫺.05, .12]

.09 .22 .27

Note. SES ⫽socioeconomic status; ISEI ⫽International Socioeconomic Index of Occupational Status at age

52; CI ⫽confidence interval. Individual income was mean-centered for males and females. Characters in bold

indicate that the CI do not include 0. Predictors were included only when they had significant bivariate

correlations with the outcome variables.

Table 3

Results of Path Analyses: Predictors of Career Success

Model 1 Model 2

Years of education

Occupational status

(ISEI) Years of education Individual income

␤95% CI ␤95% CI ␤95% CI ␤95% CI

IQ .21 [.15, .28] .10 [.03, .16] .21 [.13, .28] .15 [.07, .24]

Parental SES .24 [.17, .31] .07 [.01, .13] .28 [.20, .37] .00 [⫺.09, .07]

Years of education — — .42 [.35, .49] —— .37 [.27, .46]

School entitlement ⫺.00 [⫺.09, .07] .00 [⫺.07, .08] ⫺.02 [⫺.11, .06] ⫺.02 [⫺.11, .08]

Responsible student .02 [⫺.05, .09] .08 [.01, .14] .04 [⫺.03, 11] .05 [⫺.03, .12]

Sense of inferiority ⫺.09 [⫺.16, ⫺.01] ⫺.04 [⫺.11, 03] ⴚ.10 [ⴚ.17, ⴚ.02] ⫺.01 [⫺.10, .08]

Pessimism ⫺.05 [⫺.13, .03] ⫺.05 [⫺.12, .03] .00 [⫺.09, .09] ⫺.02 [⫺.11, .08]

Rule breaking and defiance .07 [.01, .13] — — .05 [⫺.01, .12] .12 [.04, .19]

Studiousness (teacher ratings) .19 [.12, .25] .13 [.06, .20] .20 [.13, .28] .03 [⫺.05, .12]

.29 .38 .32 .28

Note. SES ⫽socioeconomic status; ISEI ⫽International Socioeconomic Index of Occupational Status at age 52; CI ⫽confidence interval. Characters

in bold indicate that the CI do not include 0. Individual income was mean-centered for males and females.

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mism because they had no significant (in-)direct effects. With

regard to the prediction of occupational success, 39% of the effect

of sense of inferiority was mediated via educational attainment.

There was almost no mediation of the responsible student scale.

For teacher-rated studiousness, about one third of the total effect of

teacher-rated studiousness was mediated by educational attain-

ment. For childhood intelligence, almost half of the total effect was

mediated by educational attainment. The percentage of the effect

of the influence of parental SES that was mediated was almost two

thirds.

For individual income, more than half of the effect of sense of

inferiority was mediated by educational attainment. Rule breaking

and defiance of parental authority was not mediated. More than

two thirds of the effect of teacher-rated studiousness was mediated,

whereas only one third was mediated by educational attainment. In

addition, the effect of parental SES on income was almost entirely

mediated by educational attainment. In summary, the mediational

effect of educational attainment differed across the predictors.

Discussion

Drawing on a longitudinal nationally representative sample

spanning 40 years from childhood to middle adulthood, this study

was designed to add to the empirical body of knowledge on the

relations between student characteristics and behaviors in child-

hood and important life outcomes. In doing so, we showed that

some of these noncognitive student predictors operate along both

direct paths and indirect paths via educational attainment. More

important, noncognitive childhood personality characteristics were

found to predict educational attainment, occupational success, and

individual income over and above childhood IQ and parental SES.

Overall, our results show that individual differences in cognitive

and noncognitive childhood characteristics may lead to cumulative

effects on key life outcomes across the life span.

Which Childhood Characteristics Predict

Educational Attainment?

In general, we found significant relations for childhood IQ and

SES with educational attainment that is in line with the sociolog-

ical and psychological models (see Blau & Duncan, 1967;Eccles,

2005). As there is much previous research on the validity of these

predictors for educational success (e.g., Gottfredson, 2002;

Gustafsson & Undheim, 1996;Kuncel et al., 2004), we will focus

our discussion on student characteristics and behaviors.

Educational attainment was best predicted by defiance of pa-

rental authority, sense of inferiority, and teacher-rated studious-

ness. The effects were still significant after including IQ and

parental SES as predictors. This is in line with Credé and Kuncel’s

assumption of independent effects. There are different possible

explanations for the influences of the different scales.

Table 4

Standard Direct, Indirect, and Total Effects of Childhood Characteristics and Behaviors, IQ, and Parental SES at Age 12 on Career

Success at Age 52

Effect decomposition

Model 1: Occupational status (ISEI) Model 2: Individual income

␤95% CI % mediated ␤95% CI % mediated

Direct effect .10 [.03, .16] .16 [.09, .24]

Indirect effect via education .09 [.06, .12] .08 [.04, .11]

Total effect .18 [.11, .25] 48.35 .24 [.16, .32] 31.54

Parental SES

Direct effect .07 [.01, .14] .00 [⫺.08, .08]

Indirect effect via education .10 [.06, .13] .10 [.06, .15]

Total effect .17 [.11, .24] 57.56 .11 [.02, .19] 96.30

Responsible student

Direct effect .08 [.02, .14] .05 [⫺.03, .12]

Indirect effect via education .01 [⫺.02, .04] .02 [⫺.01, .04]

Total effect .09 [.02, .16] 7.49 .06 [⫺.02, .14] 25.80

Sense of inferiority

Direct effect ⫺.05 [⫺.13, .02] ⫺.02 [⫺.11, .07]

Indirect effect via education ⴚ.04 [ⴚ.07, ⴚ.01] ⴚ.04 [ⴚ.07, ⴚ.01]

Total effect ⴚ.09 [ⴚ.17, ⴚ.01] 39.33 ⫺.06 [⫺.16, .04] 62.71

Rule breaking and defiance

Direct effect .04 [⫺.02, .10] .12 [.04, .20]

Indirect effect via education .03 [.00, .05] .02 [⫺.01, .05]

Total effect .07 [.01, .13] 39.13 .14 [.06, .22] 14.29

Studiousness (teacher ratings)

Direct effect .13 [.05, .20] .03 [⫺.06, .11]

Indirect effect via education .08 [.05, .11] .08 [.04, .11]

Total effect .20 [.11, .28] 38.69 .10 [.01, .19] 73.78

Note. The total effect is the sum of the direct and indirect effects. Education was measured as years of education. SES ⫽socioeconomic status; ISEI ⫽

International Socioeconomic Index of Occupational Status at age 52; CI ⫽confidence interval. Individual income was mean-centered for males and

females; Characters in bold indicate that the CI do not include 0. Effect estimates came from the completely standardized solution of the corresponding

models. Percent mediated was calculated by dividing the total indirect effect by the total effect (cf. MacKinnon et al., 2001) but may differ slightly from

the quotient total indirect effect/total effect in Table 4 because of rounding errors.

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First, students with high rule breaking and defiance of parental

authority might be more competitive in the school context and

more visible in interactions in the classroom. This might lead to at

least higher oral grades compared with students with lower levels

of rule breaking and defiance and to more demanding and encour-

aging teacher behavior. Rosenbaum (2001) demonstrated that

teachers used not only the students’ cognitive abilities to determine

grades but also students’ noncognitive behaviors. However, be-

cause of the archival nature of the data, we consider this finding as

preliminary. Further research is needed to replicate this finding

using a more comprehensive measurement approach (see below).

Second, a high level of sense of inferiority was associated with

lower educational attainment. As the validation study (see the

online Supplementary Material) revealed, this scale encompasses a

feeling of inferiority in comparison with classmates with regard to

exercises, homework, and abilities. It is also highly related to

pessimism. Feeling inadequate in school seems to be a repressive

and inhibiting factor for educational success. Yates (2002), for

instance, demonstrated a negative link between pessimism and

achievement in mathematics. Such negative values and beliefs

seem to play an important role in learning and achievement; thus,

they are expected to negatively influence educational attainment.

In particular, maladaptive perceptions and attitudes might become

self-fulfilling prophecies as feeling inadequate in the school con-

text might lead to lower self-esteem and self-concept, both of

which are known to be related to lower school achievement

(Marsh, 1990;Nolen-Hoeksema, Girgus, & Seligman, 1986).

Third, teacher-rated studiousness was one of the most robust

predictors of educational and occupational success. The teacher

might be an especially good source of students’ studiousness

because teachers’ ratings are based on observable behavior in the

classroom and they keep track of how hard students work and

students’ willingness to learn. In a sample of high-ability males,

Kern and colleagues (2009) demonstrated that teacher and parent

ratings of Conscientiousness in childhood showed small but sig-

nificant associations with occupational success. Our results also

revealed an effect of teacher-rated studiousness on educational

attainment.

Which Childhood Characteristics Predict

Occupational Success and Income?

For predicting occupational success, several predictors were

important over and above IQ, parental SES, and educational at-

tainment: the responsible student scale and teacher-rated studious-

ness. We picked those variables as controls because they have been

shown to be the most promising candidates: Childhood IQ and

parental SES were previously shown to predict occupational suc-

cess (e.g., Bradley & Corwyn, 2002;Heckman et al., 2006;Kuncel

et al., 2004;Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007)as

predicted by Credé and Kuncel (2008). Therefore, we will focus

our discussion on student characteristics and behaviors.

We found a direct influence of the responsible student scale on

occupational success. This direct path may be accounted for by

different mechanisms or processes. Being a responsible student

may lead to higher task effectiveness (Roberts et al., 2007). There-

fore, one could imagine that individuals who work harder and are

more studious could also be more effective at accomplishing their

tasks and daily routines. Moreover, individuals who are well-

organized, act responsibly, work hard, demonstrate task persis-

tence, and complete tasks thoroughly—all of which are aspects of

the responsible student scale—are more productive in the long-

term than individuals who score lower on such skills and abilities

(Andersson & Bergman, 2011;Barrick, Mount, & Judge, 2001).

For example, Duckworth and colleagues (2007) demonstrated that

students with a high perseverance for long-term goals achieved

higher educational attainment. In addition, attitudes and work

habits were ranked as important factors for influencing hiring

decisions (Stasz, 2001). Barrick and Mount (1991) showed in their

meta-analysis that working hard and being conscientious were

associated with higher job proficiency ratings.

Furthermore, the responsible student is characterized by focus-

ing on homework and sustaining attention even when distracted.

Therefore, another mechanism that might explain the association

of the responsible student scale and midlife success outcomes

might be task persistence, which was also shown to predict career

success (Andersson & Bergman, 2011). We know from the vali-

dation study (see the online Supplementary Material) that students

who score high on the responsible student scale can also be

described as conscientious, open, and agreeable. This specific

constellation of positive characteristics and behaviors seems like a

potent combination for predicting success in life, and this is also in

line with findings by Viinikainen and colleagues (2010). They

found that constructiveness (high activity and high self-control)

was related to career success. They also demonstrated that being

reliable and reasonable in childhood led to higher success in

adulthood. Moreover, Duckworth and colleagues (2007) demon-

strated that students with a high perseverance for long-term goals

achieved higher educational attainment.

In line with the self-report results, teacher-rated studiousness

also had a direct relation to occupational success. This effect might

work through the same mechanisms that apply to the responsible

student scale, especially for studiousness, as it might be a proxy for

the adult personality trait of conscientiousness. If so, then students

who were ranked higher on studiousness in childhood would also

be higher on Conscientiousness in adulthood and might be re-

cruited into more challenging and complex jobs because of their

personality traits (see Shiner & Caspi, 2003).

More important, the results demonstrate that a substantial part of

the association between teacher-rated studiousness and occupa-

tional success across 40 years was mediated via educational at-

tainment. Educational attainment was the strongest predictor as

also predicted by Blau and Duncan (1967). The indirect path via

educational attainment may be triggered by active niche-picking

(see Roberts et al., 2007). Students choose educational experiences

and environments whose qualities match their own personalities.

Therefore, they might be more likely to choose challenging tasks

and environments, which may then lead to higher qualifications

and degrees. In turn, these environments may also reward such

industrious behaviors and conscientiousness-related traits, and

higher qualifications or educational attainment may open the door

for more prestigious and better paying jobs (see Ritchie & Bates,

2013;Schoon, 2008).

One surprising finding was that rule breaking and defiance of

parental authority was the best noncognitive predictor of higher

income after accounting for the influence of IQ, parental SES, and

educational attainment. Given the nature of our archival data, the

possible explanations are rather ad hoc and our exploratory results

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need to be replicated. This will help us better understand the

construct. If there are no other omitted third variables, we might

assume that students who scored high on this scale might earn a

higher income because they are more willing to be more demand-

ing during critical junctures such as when negotiating salaries or

raises. For instance, individuals who scored low on Agreeableness

were also shown to earn more money (Judge, Livingston, & Hurst,

2012). One explanation Judge and colleagues (2012) gave for this

finding was that it might be because of the fact that such individ-

uals value competition more than interpersonal relations and there-

fore want to advance their interests relative to others. Another

explanation might be that individuals with higher levels of rule

breaking and defiance of parental authority also have higher levels

of willingness to stand up for their own interests and aims, a

characteristic that leads to more favorable individual outcomes

(Barry & Friedman, 1998)—in our case, income. This may be one

of the reasons why defiance of parental authority plays a role in

determining income—students who show higher levels of rule

breaking and defiance are more likely to engage in negotiations

about earning and payment (see Judge at al., 2012) and fight more

strongly to achieve personal benefits. We also cannot rule out that

individuals who are likely or willing to break rules get higher pay

for unethical reasons. For instance, research in the field of orga-

nizational psychology showed that employees invest in unethical

or deviant workplace behavior when they are not satisfied with

their income and when they have a high level of love of money

(Tang & Chiu, 2003). Thus, this kind of behavior might in turn

lead to higher income. Nevertheless, further research is needed to

better understand the construct and its mechanisms.

Early Individual Differences and Cumulative

Advantages Over the Life Course

With the life span perspective of our dataset, we were able to

identify the developmental significance of individual differences

that emerge early for later important life outcomes. This supports

the more general goal of life span approaches to understand

personality across the life course (see Friedman, Kern, Hampson,

& Duckworth, 2014;Kern, Hampson, Goldberg, & Friedman,

2014). Student characteristics and behaviors were rewarded in

school and led to higher educational attainment as already stated in

sociological models (e.g., Blau & Duncan, 1967). However, above

and beyond the gatekeeping function of educational attainment,

some of these characteristics (e.g., being a responsible student and

showing defiance of parental authority) contributed to later occu-

pational success. These initial individual differences in school-

related and nonschool-related student characteristics and behav-

iors, particularly being an industrious and responsible student,

might develop into a cumulative advantage over time over and

above individual differences in education, IQ, and parental SES.

Students behave in a certain way on the basis of their character-

istics, and they experience events across the different phases of

their lives. Thereby, those characteristics can be viewed as factors

that initiate a cascade of events that will influence behavior and

decisions over a long period of time. This idea of phase-specific

and continuous processes linking personality traits (in particu-

lar: Conscientiousness) and important life outcomes (in partic-

ular: health) was also proposed by Shanahan and colleagues

(see Shanahan et al., 2014). To the extent that these qualities are

consistent over time, the behaviors that are based on these traits

(e.g., working hard) will be rewarded in educational and occu-

pational environments, and such rewards in turn may lead to

stability in showing these kinds of behaviors. In summary, this

might lead to different developmental trajectories that are in

part the result of the cumulative influences of individual dif-

ferences in childhood in the student characteristics and behav-

iors.

Strengths, Limitations, and Future Directions

Major strengths of the present work consist of the life span

longitudinal design, the inclusion of intelligence and family SES as

predictors, and the (multiperspective) assessment of student char-

acteristics and behaviors. Moreover, we assessed student charac-

teristics and behaviors at a young age— before any selection

process had begun. Therefore, it is unlikely that faking or social

desirability could have biased the estimates. Despite these

strengths, the present study has several limitations that have im-

plications for the design of future investigations.

First, teacher-rated studiousness was measured by a single item.

It is likely that this single item carries variance from multiple

dimensions. This is especially the case given how well it predicts

all outcomes in our study. Our single-item might overlap for

instance with student achievement. Nevertheless, it is a unique and

different construct because it captures also the motivational and

personality-based component of achievement. Studiousness or re-

lated constructs such as industriousness and Conscientiousness

were shown to be related to and predict achievement in earlier

studies (see Poropat, 2009). We, of course, would prefer to have a

multi-item rating scale provided by the teachers, but that was not

gathered in the original assessment. We still feel it is important to

include the single item rating, as it is unusual for studies of this

structure to have access to both self-reports and observer ratings of

any sort. In future studies we aim to examine the potential multi-

dimensional nature of the rating.

Second, because the current study involved longitudinal data

from 40 years ago, we had to rely on the questionnaires that were

administered back then because the currently more widely used

personality questionnaires such as the Big Five did not exist 40

years ago. Thus, we had to reconstruct the measurement frame-

work for the student questionnaire as the original documentation

was not available. We believe we overcame this limitation by

factor analyzing our items and then testing the reliability and

validity of our empirically derived scales. Moreover, we studied

the relations of these scales with existing questionnaires in an

additional sample. Here, we applied methods of integrative data

analysis (Curran & Hussong, 2009) by drawing on data from two

independent samples. In doing so, our goal was to make the most

of the information provided by the MPS instrument (for which no

measurement framework was available) and a more comprehen-

sive measure of personality (see online Supplementary Material).

Even if this additional step is somewhat uncommon, the results

obtained in the validation study (see the online Supplementary

Material) confirmed the validity and reliability of the scales ob-

tained from the original student questionnaire and used in the

present study. Our results showed that another challenge of future

research will be to incorporate existing longitudinal data with

reliable, new measures of personality. Moreover, our results need

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to be replicated as our findings were demonstrated in a specific

population (students in the 1960s) and a specific educational

system (Luxembourg).

One avenue for future research should be to provide a closer

examination of the relation between individual differences and

educational research. Theories about personality development

and knowledge about educational environments such as school

should be connected to identify possible influences on stability and

change in student characteristics and behaviors within the school

context. Students spend the majority of their adolescent lives in

school. Some of these environments probably have the potential to

shape personality development. In a next step, the important ele-

ments of these environments need to be identified and tested.

Conclusion

To conclude, student characteristics and behaviors play signif-

icant roles in important life outcomes over and above socioeco-

nomic factors and cognitive abilities. We demonstrated that being

successful is more than “just” having good cognitive resources and

coming from a socially advantaged family and that personality-

related characteristics and student behavior measured early in life

are important predictors of life outcomes in midlife. This coherent

pattern of results strengthens the power of personality and

personality-related traits in the context of real-life outcomes. Con-

sidering that several traits and characteristics that were previously

demonstrated to be predictive of educational attainment and career

success (e.g., parental SES and intelligence) were controlled for,

the incremental and independent influences of student behavior

and childhood personality characteristics—measured early in

life—add to our understanding of which factors in late childhood

are important for successful adaptation in middle adulthood.

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Received September 12, 2014

Revision received May 8, 2015

Accepted May 20, 2015 䡲

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Objective To investigate the effects of being born late preterm (LPT, 34–36 weeks’ gestation) or early term (37–38 weeks) on children’s educational achievement between ages 5 and 11 years. Design A series of observational studies of longitudinal linked health and education data. Setting The Born-in-Bradford (BiB) birth cohort study, which recruited mothers during pregnancy between 2007 and 2011. Participants The participants are children born between 2007 and 2011. Children with missing data, looked-after-children, multiple births and births post-term were excluded. The sample size varies by age according to amount of missing data, from 7860 children at age 5 years to 2386 at age 11 years (8031 at age 6 years and 5560 at age 7 years). Main outcome measures Binary variables of whether a child reached the ‘expected’ level of overall educational achievement across subjects at the ages of 5, 6, 7 and 11 years. The achievement levels are measured using standardised teacher assessments and national tests. Results Compared with full-term births (39–41 weeks), there were significantly increased adjusted odds of children born LPT, but not early term, of failing to achieve expected levels of overall educational achievement at ages 5 years (adjusted OR (aOR) 1.72,95% CI 1.34 to 2.21) and 7 years (aOR 1.46, 95% CI 1.08 to 1.97) but not at age 11 years (aOR 1.51, 95% CI 0.99 to 2.30). Being born LPT still had statistically significant effects on writing and mathematics at age 11 years. Conclusions There is a strong association between LPT and education at age 5 years, which remains strong and statistically significant through age 11 years for mathematics but not for other key subjects.

The longitudinal interplay of self-esteem, social relationships, and academic achievement during adolescence: Theoretical notions and bivariate meta-analytic findingsDas längsschnittliche Zusammenspiel von Selbstwert, sozialen Beziehungen und schulischen Leistungen in der Adoleszenz: Theoretische Überlegungen und bivariate meta-analytische Befunde

Article

Full-text available

Aug 2023

Self-esteem and social relationships have been recognized as key socio-emotional predictors of diverse life-outcomes in adolescence and beyond. Despite such recognitions, fairly little is known about their longitudinal interplay with academic variables and thus, to what degree educational outcomes are interrelated with these socio-emotional dynamics during adolescence. Whereas the interplay between self-esteem and social relationships is already well documented in developmental and personality psychological research, the goal of the current paper is to extend this bivariate picture by including the educational perspective. Specifically, the aim of the current study is twofold: From a theoretical point of view, we aim to integrate developmental, differential, and educational concepts to emphasize the importance of a joint consideration of self-esteem, social relationships, and academic achievement during adolescence. Empirically, we aim to present a meta-analytic overview on existing longitudinal studies to illustrate the current knowledge and highlight remaining research gaps in the current adolescent literature. We conclude by outlining important directions for future research.

Cross-National Harmonization of Neurocognitive Assessment Across Five Sites in a Global Study

Article

Jul 2022

Objective: Cross-national work on neurocognitive testing has been characterized by inconsistent findings, suggesting the need for improved harmonization. Here, we describe a prospective harmonization approach in an ongoing global collaborative study. Method: Visuospatial N-Back, Tower of London (ToL), Stop Signal task (SST), Risk Aversion (RA), and Intertemporal Choice (ITC) tasks were administered to 221 individuals from Brazil, India, the Netherlands, South Africa, and the USA. Prospective harmonization methods were employed to ensure procedural similarity of task implementation and processing of derived task measures across sites. Generalized linear models tested for between-site differences controlling for sex, age, education, and socioeconomic status (SES). Associations with these covariates were also examined and tested for differences by site with site-by-covariate interactions. Results: The Netherlands site performed more accurately on N-Back and ToL than the other sites, except for the USA site on the N-Back. The Netherlands and the USA sites performed faster than the other three sites during the go events in the SST. Finally, the Netherlands site also exhibited a higher tolerance for delay discounting than other sites on the ITC, and the India site showed more risk aversion than other sites on the RA task. However, effect size differences across sites on the five tasks were generally small (i.e., partial eta-squared < 0.05) after dropping the Netherlands (on ToL, N-Back, ITC, and SST tasks) and India (on the RA task). Across tasks, regardless of site, the N-Back (sex, age, education, and SES), ToL (sex, age, and SES), SST (age), and ITC (SES) showed associations with covariates. Conclusions: Four out of the five sites showed only small between-site differences for each task. Nevertheless, despite our extensive prospective harmonization steps, task score performance deviated from the other sites in the Netherlands site (on four tasks) and the India site (on one task). Because the procedural methods were standardized across sites, and our analyses were adjusted for covariates, the differences found in cognitive performance may indicate selection sampling bias due to unmeasured confounders. Future studies should follow similar cross-site prospective harmonization procedures when assessing neurocognition and consider measuring other possible confounding variables for additional statistical control. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A comparison of methods to test the mediation and other intervening variable effects

Article

Full-text available

Jan 2002
PSYCHOL METHODS

A Monte Carlo study compared 14 methods to test the statistical significance of the intervening variable effect. An intervening variable (mediator) transmits the effect of an independent variable to a dependent variable. The commonly used R. M. Baron and D. A. Kenny (1986) approach has low statistical power. Two methods based on the distribution of the product and 2 difference-in-coefficients methods have the most accurate Type I error rates and greatest statistical power except in 1 important case in which Type I error rates are too high. The best balance of Type I error and statistical power across all cases is the test of the joint significance of the two effects comprising the intervening variable effect.

Motivational Beliefs, Values and Goals

Article

Full-text available

Feb 2002

This chapter reviews the recent research on motivation, beliefs, values, and goals, focusing on developmental and educational psychology. The authors divide the chapter into four major sections: theories focused on expectancies for success (self-efficacy theory and control theory), theories focused on task value (theories focused on intrinsic motivation, self-determination, flow, interest, and goals), theories that integrate expectancies and values (attribution theory, the expectancy-value models of Eccles et al., Feather, and Heckhausen, and self-worth theory), and theories integrating motivation and cognition (social cognitive theories of self-regulation and motivation, the work by Winne & Marx, Borkowski et al., Pintrich et al., and theories of motivation and volition). The authors end the chapter with a discussion of how to integrate theories of self-regulation and expectancy-value models of motivation and suggest new directions for future research.

Primary Mental Abilities

Chapter

Jan 1973

L. L. Thurstone

This publication is the opening number of a series which the Psychometric Society proposes to issue. It reports the first large experimental inquiry, carried out by the methods of factor analysis described by Thurstone in The Vectors of the Mind 1. The work was made possible by financial grants from the Social Science Research Committee of the University of Chicago, the American Council of Education, and the Carnegie Corporation of New York. The results are eminently worthy of the assistance so generously accorded. Thurstone’s previous theoretical account, lucid and comprehensive as it is, is intelligible only to those who have a knowledge of matrix algebra. Hence his methods have become known to British educationists chiefly from the monograph published by W. P. Alexander8. This enquiry has provoked a good deal of criticism, particularly from Professor Spearman’s school ; and differs, as a matter of fact, from Thurstone’s later expositions. Hence it is of the greatest value to have a full and simple illustration of his methods, based on a concrete inquiry, from Professor Thurstone himself.

Ein Vergleich von HAWIE und LPS bei der psychometrischen Einzelfalldiagnostik neurologischer Patienten

Article

Jan 1982

The Effects of Cognitive and Non-Cognitive Abilities on Labor Market Outcomes and Social Behavior

Article

Jul 2006
J LABOR ECON

This article establishes that a low-dimensional vector of cognitive and noncognitive skills explains a variety of labor market and behavioral outcomes. Our analysis addresses the problems of measurement error, imperfect proxies, and reverse causality that plague conventional studies. Noncognitive skills strongly influence schooling decisions and also affect wages, given schooling decisions. Schooling, employment, work experience, and choice of occupation are affected by latent noncognitive and cognitive skills. We show that the same low-dimensional vector of abilities that explains schooling choices, wages, employment, work experience, and choice of occupation explains a wide variety of risky behaviors.

The Development of Meaning

Article

Jan 1978

Beyond College For All: Career Paths for the Forgotten Half

Article

Nov 2002

On Effect Size

Article

Apr 2012
PSYCHOL METHODS

The call for researchers to report and interpret effect sizes and their corresponding confidence intervals has never been stronger. However, there is confusion in the literature on the definition of effect size, and consequently the term is used inconsistently. We propose a definition for effect size, discuss 3 facets of effect size (dimension, measure/index, and value), outline 10 corollaries that follow from our definition, and review ideal qualities of effect sizes. Our definition of effect size is general and subsumes many existing definitions of effect size. We define effect size as a quantitative reflection of the magnitude of some phenomenon that is used for the purpose of addressing a question of interest. Our definition of effect size is purposely more inclusive than the way many have defined and conceptualized effect size, and it is unique with regard to linking effect size to a question of interest. Additionally, we review some important developments in the effect size literature and discuss the importance of accompanying an effect size with an interval estimate that acknowledges the uncertainty with which the population value of the effect size has been estimated. We hope that this article will facilitate discussion and improve the practice of reporting and interpreting effect sizes.

Subjective Task Value and the Eccles et al. Model of Achievement-Related Choices

Chapter

Jan 2005

Jacquelynne S Eccles

(Ed.), The following values have no corresponding Zotero field: ID - 534

Education, Opportunity, and Social Inequality: Changing Prospects in Western Society

Article

Mar 1976

Student Characteristics and Behaviors at Age 12 Predict Occupational Success 40 Years Later Over and Above Childhood IQ and Parental Socioeconomic Status

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