io

To

Career successIncomeJob satisfaction

possearndiandaspiry sodelavio

income associated with ones job; job complexity predicted income and job satisfaction; and income pre-

regulaively eg cliniealthge basese arei, 1990

with this idea, dispositional self-control has been linked to a

outcomes (e.g., Tangney, Baumeister, & Boone, 2004; Wolfe &Johnson, 1995).

However, dispositional self-control has not received substantialattention in work contexts, particularly in terms of career success.Given the pervasive inuence of this characteristic on signicant

n of contr00, p. 247

concept thus entails internally focused active control tendinvolving regulation of thoughts, feelings, or behaviors. Dtional self-control has connections to other prominent perstraits, including the Big Five, but evidence indicates this character-istic is reasonably distinct. There is, for example, a clear link be-tween self-control and conscientiousness but thesecharacteristics appear to overlap only partially (e.g., Tangneyet al., 2004, reported a correlation of .54 between self-controland conscientiousness).

Career success has been conceptualized in a variety of waysbut is often dened as the positive psychological or work-related

Corresponding author. Address: School of Psychology, Florida Institute ofTechnology, 150 W. University Blvd., Melbourne, FL 32901-6975, United States.Tel.: +1 (321) 674 8104.

Personality and Individual Differences 59 (2014) 6570

Contents lists availab

Personality and Indi

.eE-mail address: pconvers@t.edu (P.D. Converse).number of important outcomes including academic performance,impulse control, psychological adjustment, and interpersonal

Self-control has been dened as the exertiothe self by the self (Muraven & Baumeister, 200191-8869/$ - see front matter 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.paid.2013.11.007ol over). Thisenciesisposi-onalitytained research attention stems in part from the notion that inef-fective self-control is a major factor responsible for a range ofpersonal and social problems (e.g., Baumeister, Heatherton, & Tice,1994) and conversely effective self-control is a key to success in life(e.g., Baumeister, Leith, Muraven, & Bratslavsky, 1998). Consistent

velop and test a model of the pathways leading from self-control tocareer outcomes.

1.1. Dening self-control and career success1. Introduction

Self-controlinvolving effectiveings, and behaviorshas been extensof contexts. Researchers investigatincial, personality, criminology, and hhave developed substantial knowledand implications of self-control in th& Vohs, 2004; Gottfredson & Hirschdicted job satisfaction. These ndings add to research on self-control and career success, further demon-strating the relevance of self-control in this context and highlighting key links connecting these variablesinvolving factors related to start and stop control.

2013 Elsevier Ltd. All rights reserved.

tion of thoughts, feel-xamined across a rangecal, developmental, so-issues (among others),es regarding the natureas (e.g., see Baumeister). This broad and sus-

behaviors and outcomes, this may represent a notable gap inresearch on the factors inuencing career-related outcomes. Thepurpose of the present study was to extend the limited researchon this issue. More specically, this research draws from two the-oretical perspectivesinvolving the concepts of cumulative andinteractional continuity (Caspi, Bem, & Elder, 1989) and the recentnotion of a distinction between two aspects of self-control (stop/inhibitory and start/initiatory; de Boer, van Hooft, & Bakker,2011; de Ridder, de Boer, Lugtig, Bakker, & van Hooft, 2011)to de-Inhibitory controlAdolescent behavior

and job complexity. Results indicated that childhood self-control predicted positive and negative adoles-cent behavior; this behavior predicted educational attainment; education predicted the complexity andChildhood self-control, adolescent behav

Patrick D. Converse , Katrina A. Piccone, Michael C.School of Psychology, Florida Institute of Technology, United States

a r t i c l e i n f o

Article history:Received 7 June 2013Received in revised form 1 November 2013Accepted 9 November 2013Available online 5 December 2013

Keywords:Self-controlInitiatory control

a b s t r a c t

Research indicates that disand outcomes but little rework adds to the limited ing a model of self-controlinteractional continuity (Cinitiatory and stop/inhibitodeveloped and tested a mfocusing on adolescent beh

journal homepage: wwwitional self-control is an important predictor of a wide range of behaviorsch has examined this characteristic in the context of career success. Thisngs in this area and extends previous research by developing and examin-career success. Specically, drawing from the concepts of cumulative and, Bem, & Elder, 1989) and the recently proposed distinction between start/elf-control (e.g., de Ridder, de Boer, Lugtig, Bakker, & van Hooft, 2011), weof the pathways leading from childhood self-control to career outcomesr that is positive (e.g., studying) versus negative (e.g., stealing), education,r, and career success

cci

le at ScienceDirect

vidual Differences

lsevier .com/locate /paid

Indioutcomes accumulated as a result of ones work experiences(e.g., Judge, Cable, Boudreau, & Bretz, 1995; Ng, Eby, Sorensen, &Feldman, 2005). Career-related success is typically characterizedas involving two primary components: extrinsic or objective andintrinsic or subjective (see Ng et al., 2005). Extrinsic career successinvolves observable outcomes such as salary, whereas intrinsiccareer success involves subjective outcomes such as satisfaction.Given previous evidence suggesting that these two types ofoutcomes are relatively independent (Ng et al., 2005), the currentstudy examined both extrinsic and intrinsic outcomes, focusingon income and job satisfaction.

1.2. Linking self-control and career success

As noted, although a large number of studies have demon-strated connections between self-control and a variety of impor-tant behaviors and outcomes, research on this characteristic inthe career context has been quite limited. For instance, in a recentmeta-analysis, de Ridder, Lensvelt-Mulders, Finkenauer, Stok, andBaumeister (2012) reviewed evidence regarding the relationshipbetween trait self-control and behaviors across a range of domains.These researchers reported k (number of tests) of only 5 for the do-main school and work performance. Furthermore, although deRidder et al. did not provide detailed information regarding thespecic behaviors examined in the school and work performancedomain, it may be that these were largely school- or task-related,rather than work- or career-related, as these authors listed GPA,homework hours, and persistence at solving tasks as examples ofbehaviors in this domain. There have been a few studies examiningrelationships between self-control variables and unemployment(e.g., Kokko, Pulkkinen, & Puustinen, 2000) and career orienta-tion (involving occupational status, education, present work situ-ation, and career stability; Pulkkinen, Ohranen, & Tolvanen, 1999),but research investigating specic career success outcomes in em-ployed individuals has been limited.

Two recent exceptions are studies by Moftt et al. (2011) andConverse, Pathak, DePaul-Haddock, Gotlib, and Merbedone(2012). Moftt et al. (2011) demonstrated that childhood self-con-trol (beginning at age 3) predicted negative adolescent behaviors(from age 13 to 21; e.g., smoking and dropping out of school),which, in turn, predicted income later in life (at age 32). Similarly,Converse et al. (2012) found that childhood self-control related tolater income and occupational prestige through educational attain-ment and later career satisfaction through occupational opportu-nity for achievement. These studies provide valuable initialevidence of the relevance of self-control in predicting career-re-lated outcomes. However, information regarding the specic path-ways linking self-control to these outcomes is somewhat limitedand, in particular, the above studies did not draw from the recentperspective suggesting that there are two aspects of self-control(start and stop). Thus, this work draws from Caspi and colleagues(1989) ideas regarding cumulative and interactional continuity andthe notions of start and stop self-control to develop and test a mod-el involving self-control measured in childhood, positive and neg-ative behaviors (related to start and stop control) in adolescence,and career-related outcomes in adulthood (see Fig. 1). Note thatthese perspectives provided the stimulus and conceptual back-ground for the current research but we are not directly testingthese theories.

1.2.1. Cumulative and interactional continuityThe model developed in the present research is developmental

in that it involves pathways leading from self-control relatively

66 P.D. Converse et al. / Personality andearly in life to career outcomes experienced in adulthood. To devel-op this model, we drew from Caspi and colleagues (e.g., Caspi, Rob-erts, & Shiner, 2005; Caspi et al., 1989) ideas regarding continuitiesand consequences of interactional styles over time. Caspi et al.(1989) proposed two general processes involving person-environ-ment interactions across time. First, cumulative continuity in-volves the individual selecting, creating, and shaping his/herenvironments based partially on dispositional qualities, and theseenvironments then sustaining those dispositions. An extravertedindividual, for example, may often select environments involvingsocial interaction; these environments may then help to maintainthis individuals extraverted tendencies. Over long periods of time,this type of process may lead to not only stable dispositions, butalso distinct life paths. Second, interactional continuity involvestransactions between the individual and the environment: theindividual acts, the environment reacts, and the individual re-sponds. Caspi et al. (1989) proposed that this process can also haveimplications for life paths and dispositional continuity in severalways including through reciprocal reinforcement, self-conrmingexpectations, and selective attention to information that conrmsones self-concept.

In developing these ideas, Caspi and colleagues emphasizedcontinuity with respect to certain interactional styles, but theseprocesses also appear to be quite relevant to the current contextinvolving pathways leading to career outcomes. More specically,the concepts of cumulative and interactional continuity suggestthat individual differences in an inuential characteristic such asself-control present relatively early in development can producedifferent life paths, resulting in different career outcomes. For in-stance, self-control may inuence the types of environments indi-viduals select, create, and shape (e.g., those higher in self-controlmay choose situations based on longer term implications ratherthan shorter term rewards). Similarly, this trait may also affectindividuals reciprocal interactions with the environment (e.g.,those higher in self-control may engage in more long-term desir-able behaviors that are then reinforced by the environment includ-ing parents and teachers). As these processes unfold over time,different life paths may be created, leading to differing career out-comes. Fig. 1 presents the current model based on this perspectiveand the following sections discuss the specic links.

1.2.2. Start and stop controlEffectively exercising self-control involves both inhibiting

undesirable behaviors and initiating desirable behaviors (Baumei-ster, Bratslavsky, Muraven, & Tice, 1998; de Ridder et al., 2011).Based on this notion, recent research by de Boer et al. (2011) andde Ridder et al. (2011) suggests two key dimensions of self-control:start (or initiatory) and stop (or inhibitory). The start dimension ofself-control is expected to facilitate engaging in positive behaviors,such as goal-directed activities, that may not be desirable in theshort-term but are likely to benet individuals in the long-term.Alternatively, the stop dimension of self-control is expected to in-hibit engaging in negative behaviors, such as illicit or harmfulactivities, that may be desirable in the short-term but can havenegative consequences in the long-term. Based on this research,it is likely that dispositional self-control predicts both positiveand negative behaviors. Indeed, previous research has demon-strated that self-control is positively related to positive behaviors,such as time spent studying, and negatively related to negativebehaviors, such as alcohol consumption and cigarette smoking(de Boer et al., 2011; de Ridder et al., 2011). This has also been sup-ported by a recent meta-analysis that found self-control predictsboth desirable and undesirable behaviors (de Ridder et al., 2012).Based on the concepts of cumulative and interactional continuity,these relationships are expected to hold over time, from childhoodto adolescence. For example, children higher in self-control may be

vidual Differences 59 (2014) 6570more likely to choose situations and behaviors that are seen asmore long-term desirable (e.g., following rules). This may then berewarded by parents and teachers, reinforcing those tendencies

lead to more academic success. Clearly, this greater academic per-

Education Job Complexity

.04**(.10).04**(.10) 2.25**(.13)2.25**(.13)

.18**(.39).18**(.39)

are

Indiformance is then likely to be associated with greater ability to con-tinue ones education (e.g., better grades increase the chances ofadmission to undergraduate institutions and graduate programs).Second, these behaviors may also inuence motivation to continuewith school. The idea of interactional continuity suggests how thiscan occur. If, for example, an adolescent decides to do drugs or skipclass, the environment is likely to react negatively (e.g., criticismfrom teachers or parents). This change in environment may thenover time such that they are sustained into adolescence, manifest-ing in more positive behaviors and fewer negative behaviors (interms of long-term desirability).

Hypothesis 1. Childhood self-control (a) positively relates topositive adolescent behavior and (b) negatively relates to negativeadolescent behavior.

1.2.3. Positive and negative behavior and educationThese behaviors are likely to then have implications for educa-

tional attainment. First, success in school appears to require bothinitiating these long-term desirable behaviors (e.g., studying) andinhibiting these long-term undesirable behaviors (e.g., drinking/drug use). Thus, engaging in more positive behaviors and fewernegative behaviors (with respect to long-term goals) is likely to

Self-Control

Negative Behaviors

-.05**(-.10)-.05**(-.10) -3.10**(-.23)-3.10**(-.23)

Fig. 1. Model and path analysis results. Unstandardized coefcientsPositive BehaviorsP.D. Converse et al. / Personality andhave negative effects on the students subsequent motivation(e.g., trying less in school). In contrast, if an adolescent decides toavoid drugs and attend class regularly, the environment is likelyto react positively; this may then have positive effects on subse-quent motivation. Thus, these positive and negative behaviorslikely inuence both the ability and motivation to continue withschool, which should inuence educational attainment. Consistentwith this, there has been ample research showing how positivebehaviors can facilitate educational attainment (e.g., Lleras, 2008;Staff & Mortimer, 2007) and how negative behaviors can deter edu-cational attainment (e.g., Moftt, Caspi, Harrington, & Milne, 2002).

Hypothesis 2. Positive adolescent behavior positively relates toeducational attainment.

Hypothesis 3. Negative adolescent behavior negatively relates toeducational attainment.

1.2.4. Education, job complexity, income, and satisfactionIndicators of career success, including income and job satisfac-

tion, are likely to be predicted by education and job complexity.First, educational achievement may be an important factor inu-encing career success. According to a human capital perspective,higher education levels signal that individuals have desirable attri-butes, such as intelligence and self-motivation (Ng et al., 2005).Organizations may attempt to gain and retain individuals withthese desirable attributes by offering higher wages and moreworkplace opportunities. Congruent with these expectations, edu-cation level has been found to relate to salary (Ng et al., 2005).Additionally, individuals with higher education levels are likelyto be better prepared for complex jobs, which involve intellectualdemands (Oswald, Campbell, McCloy, Rivkin, & Lewis, 1999).

Hypothesis 4. Education positively relates to (a) job complexityand (b) income.

Second, job complexity may also inuence career outcomes.Highly complex jobs involve lack of routine repetitive work in fa-vor of work involving high intellectual demands and/or frequentchanges in task-related requirementsoften involving the synthe-sis or interpretation of complex data (Oswald et al., 1999, p. 3).Put differently, these jobs involve a high degree of difculty, whichis likely to relate to higher salaries. Indeed, previous research hasillustrated a job complexity-income relationship (Judge, Klinger,& Simon, 2010). Furthermore, the job characteristics model sug-gests that complex jobs predict positive outcomes such as mean-

Job Satisfaction

.08**(.09).08**(.09)

shown rst, with standardized coefcients in parentheses. p < .01..69**(.12).69**(.12)Income

.01**(.09).01**(.09).05**(.33).05**(.33)

vidual Differences 59 (2014) 6570 67ingfulness of work, responsibility for outcomes, and knowledgeof results (Hackman & Oldham, 1980), which positively impactjob satisfaction (Judge, Bono, & Locke, 2000). Finally, income is ex-pected to relate to job satisfaction, as higher income is associatedwith self and others perceptions of success (Ng et al., 2005). In-deed, previous research has supported this income-satisfactionrelationship (see Ng et al., 2005).

Hypothesis 5. Job complexity positively relates to (a) income and(b) job satisfaction.

Hypothesis 6. Income positively relates to job satisfaction.

2. Method

2.1. Participants

Data for this study came from the US Department of Laborsponsored National Longitudinal Survey of Youth 1979 (NLSY79)Children and Young Adults database. The NLSY79 Children andYoung Adults database was started in 1986 and focuses on the

children of the female participants from the original 1979 study.Individuals in this database have been assessed every two yearssince 1986 (the most recent data available are from 2010). Thisdatabase includes information provided by the mothers aboutthe children and self-reports from children aged 10 and older.

In the current study, participants were 4932 individuals whohad scores on at least two of the focal variables and were at least20 years of age (in 2010). Note, however, that sample size variedacross variables (see Table 1). The sample was 50% female; 56%Black or Hispanic, 44% Non-Black/Non-Hispanic; and the meanapproximate age as of 2010 was 25.64 (SD = 3.63).

2.2. Procedures and measures

occupations. More specically, a crosswalk between the Censuscodes and ONET-SOC codes was used to identify correspondingONET occupations. Job complexity was then measured with theONET variable Job Zone, which represents the amount of educa-tion, experience, and training required to perform the job andranges from 1 (little or no preparation needed) to 5 (extensivepreparation needed). Job Zones were developed using SpecicVocational Preparation from the Dictionary of Occupational Titles,and evidence supports the validity of this index (see Oswald et al.,1999). Using Job Zone as an indicator of job complexity is consis-tent with previous research (e.g., Le et al., 2011). Note that in thecrosswalk more than one ONET occupation was linked to a givenCensus occupation in several cases. In those cases, average JobZone scores across the multiple ONET occupations were used.

3

68 P.D. Converse et al. / Personality and Individual Differences 59 (2014) 6570Self-control was measured with 21 items (a = .87) from theBehavior Problems Index (BPI; see Zill, 1990). These items havebeen included in various measures of self-control (e.g., Chapple,2005; McGloin, Pratt, & Maahs, 2004; Nofziger, 2008; Raffaelli,Crockett, & Shen, 2005) and signicantly predicted theoretically re-lated constructs such as peer relationships and delinquency. Addi-tionally, several of these items (e.g., He/She has difcultyconcentrating, cannot pay attention for long; He/She is impulsive,or acts without thinking) are very similar to those included in pre-vious measures of start and stop self-control that, consistent withexpectations, signicantly predicted engagement in positive andnegative behaviors (de Boer et al., 2011; de Ridder et al., 2011).Mothers rated their children on these items in 1988. Responseswere based on a three-point scale ranging from 1 (often true) to 3(not true). Lower scores indicated lower self-control.

Positive and negative behaviors were measured in 1996. Basedon items used in previous research (de Boer et al., 2011; de Ridderet al., 2011), negative adolescent behavior was measured with 11items focusing on behaviors that may be more attractive in theshort-term but less desirable in the long-term (involving lying,stealing, damaging property, drinking, using drugs, smoking, skip-ping school, and staying out without permission), and positive ado-lescent behavior was measured with seven items focusing onbehaviors that may be less attractive in the short-term but moredesirable in the long-term (involving spending time on homeworkduring school, after school, or during the summer, belonging toclubs/teams/activities, and working for pay). A log transformationwas used for both of these variables due to skew.

Educational attainment was measured as the highest gradecompleted as of 2010, specied as rst grade (coded 1) througheighth year of college or more (coded 20). Census codes for partic-ipants current or most recent occupations in 2010 were also avail-able. The Occupational Information Network (ONET) was used toobtain the level of complexity associated with each of those

Table 1Descriptive statistics and correlations.

N M SD 1 2

1 Self-control 2182 2.52 0.312 Positive behavior 3382 0.25 0.12 .10**

3 Negative behavior 1741 0.13 0.13 .10** .034 Education 4682 12.85 2.07 .17** .14**

5 Job complexity 4018 2.31 0.94 .10** .12**

6 Income 1672 3.00 0.14 .09* .20**

7 Job satisfaction 4174 3.17 0.84 .02 .018 Age 4932 25.64 3.63 .02 .32**9 Race 4932 0.44 0.50 .02 .0310 Gender 4932 0.50 0.50 .10** .04*

11 Conscientiousness 4661 5.80 1.15 .05* .05**Note: Race coded 0 = Black or Hispanic, 1 = Non-Black/Non-Hispanic. Gender coded 0 = m* p < .05.** p < .01.Income was measured as hourly rate of pay assessed in 2010 forthe respondents current or most recent job (a log transformationwas used for this variable). Job satisfaction was measured withone item in 2010 that assessed how the individual felt about his/her current or most recent job, with responses ranging from 1 (likeit very much) to 4 (dislike it very much). Previous research has sup-ported the use of single-itemmeasures of job satisfaction (Wanous,Reichers, & Hudy, 1997). Scores were recoded so that lower valuesindicated lower satisfaction.

3. Results

Table 1 presents descriptive statistics and correlations. Toexamine the hypotheses, a path analysis was conducted usingAmos. The model tested is that shown in Fig. 1 with two additions:age, race (Hispanic or Black vs. Non-Hispanic, Non-Black), gender,and conscientiousness (measured in 2010 using the Ten-Item Per-sonality Inventory; Gosling, Rentfrow, & Swann, 2003) were in-cluded as predictors of all endogenous variables, andrelationships between all exogenous variables were included.Model t indices indicated reasonably good t: v2(12) = 68.37,p < .01; NFI = .973; CFI = .977; RMSEA = .031. The model v2 was sig-nicant but this is affected by sample size and the current sampleis relatively large; the other index values appear to indicate goodt (e.g., see Kline, 2005). We also examined a model in which thecontrol variables (age, race, gender, and conscientiousness) wereremoved. Model t indices indicated somewhat poorer but stillreasonable t: v2(12) = 116.63, p < .01; NFI = .913; CFI = .920;RMSEA = .042.

As shown in Fig. 1, all hypotheses were supported. Similar re-sults were obtained for the model excluding the control variablesin that all of the coefcients were signicant, the same sign, andsimilar in magnitude. In addition, Table 2 shows proportion of var-iance accounted for in the endogenous variables ranged from .04 to

4 5 6 7 8 9 10

.21**

.08** .40**

.11* .20** .37**

.01 .02 .14** .16**

.23** .05** .21** .33** .06**

.08** .16** .05** .10** .04** .10**

.05* .12** .04** .02 .03* .01 .01

.00 .06** .08** .03 .06** .05** .09** .07**ale, 1 = female.

These ndings build on prior research in several ways. As men-

Judge, T. A., Bono, J. E., & Locke, E. A. (2000). Personality and job satisfaction:

Inditioned, one contribution of this study is that it expands upon pre-vious research by examining the role of dispositional self-control inthe context of career success and exploring the specic pathwaysthat link self-control to career-related factors. Furthermore, previ-ous research has mainly focused on self-control predicting positiveor negative behaviors and few studies have provided evidence thatself-control has similar effects on both (de Ridder et al., 2012). This.31 for the model including the control variables and .01 to .17 forthe model excluding the control variables. Thus, the predicted rela-tionships were found and in several cases the predictors accountedfor a reasonable amount of variance in the outcomes. Finally,similar results were also obtained when no variable transforma-tions were used.

4. Discussion

Research suggests that self-control predicts a variety of notablebehaviors and outcomes. Little prior work, however, has examinedself-control in the context of career success. The goal of this studywas to add to the limited work in this area, focusing on the path-ways leading to career outcomes.

4.1. Findings and implications

Results supported the role of self-control and positive and neg-ative behaviors in predicting career-related outcomes. Specically,dispositional self-control measured during childhood predictedboth positive and negative adolescent behaviors. These behaviorsin turn differentially predicted educational attainment, where indi-viduals engaging in positive behavior during adolescence weremore likely to achieve higher levels of education and individualsengaging in negative behavior during adolescence were less likelyto achieve higher levels of education. Educational attainment thenpredicted job complexity and both education and job complexitywere directly related to income. Finally, consistent with the model,both job complexity and income predicted job satisfaction.

Table 2Squared multiple correlations for endogenous variables.

Variable Including controlvariables

Excluding controlvariables

Negative behaviors .31 .01Positive behaviors .20 .02Education .10 .06Job complexity .20 .16Income .28 .17Job satisfaction .04 .03

Note: Control variables were age, race, gender, and conscientiousness.

P.D. Converse et al. / Personality andstudy demonstrated empirical links to both positive and negativebehaviors as well as implications of these behaviors.

4.2. Limitations and future directions

Future research might address some of the limitations of thisstudy. First, based on the squared multiple correlations, it is clearthat other explanatory factors are involved and future researchcould explore these factors (e.g., more integrated models incorpo-rating the current predictors along with other key explanatory fac-tors could be examined). For example, the current research focusedon individual factors but social environmental factors may also beinuential in this context. Future studies might incorporate bothtypes of factors to examine their roles in predicting career-relatedoutcomes. Second, several of the measures were somewhat limitedThe mediating role of job characteristics. Journal of Applied Psychology, 85,237249.

Judge, T. A., Cable, D. M., Boudreau, J. W., & Bretz, R. D. (1995). An empiricalinvestigation of the predictors of executive career success. Personnel Psychology,48, 485519.

Judge, T. A., Klinger, R. L., & Simon, L. S. (2010). Time is on my side: Time, generalmental ability, human capital, and extrinsic career success. Journal of AppliedPsychology, 95, 92107.

Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.).New York: Guilford Press.

Kokko, K., Pulkkinen, L., & Puustinen, M. (2000). Selection into long-termunemployment and its psychological consequences. International Journal ofBehavioral Development, 24, 310320.

Le, H., Oh, I.-S., Robbins, S. B., Ilies, R., Holland, E., & Westrick, P. (2011). Too much ofa good thing: Curvilinear relationships between personality traits and jobperformance. Journal of Applied Psychology, 96, 113133.

Lleras, C. (2008). Do skills and behaviors in high school matter? The contribution ofnoncognitive factors in explaining differences in educational attainment andearnings. Social Science Research, 37, 888902.

McGloin, J. M., Pratt, T. C., & Maahs, J. (2004). Rethinking the IQ-delinquencyrelationship: A longitudinal analysis of multiple theoretical models. JusticeQuarterly, 21, 603635.(e.g., the types of adolescent behaviors investigated) due to the useof an existing database. This is a tradeoff often associated with theuse of this type of database, where the dataset allows for examina-tion of pathways over time but limits what measures can be used.Future research might conrm and build on the current study withcross-sectional designs involving more extensive measures orother existing databases involving different measurementstrengths and weaknesses. Third, this study did not examine startand stop self-control in the form of two separate measures also be-cause of limitations involved in using an existing database. Futureresearch may benet from the development and implementationof start and stop measures of self-control in predicting career-related outcomes. Finally, missing data might be an issue in thisstudy as there is some potential for nonrandommissing data. Addi-tional studies involving different databases or other approaches todealing with missing data may be useful.

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70 P.D. Converse et al. / Personality and Individual Differences 59 (2014) 6570

Childhood self-control, adolescent behavior, and career success1 Introduction1.1 Defining self-control and career success1.2 Linking self-control and career success1.2.1 Cumulative and interactional continuity1.2.2 Start and stop control1.2.3 Positive and negative behavior and education1.2.4 Education, job complexity, income, and satisfaction

2 Method2.1 Participants2.2 Procedures and measures

3 Results4 Discussion4.1 Findings and implications4.2 Limitations and future directions

References