ese a APsychologicalApproachto HumanCapital r ETSRR–18-30 c · 2019-01-18 · ETS Research Report Series ISSN 2330-8516 RESEARCH REPORT A Psychological Approach to Human Capital

Research

Rep

ort

A Psychological Approach toHuman Capital

ETS RR–18-30

Harrison J. KellSteven B. Robbins

Rong SuMeghan Brenneman

December 2018

ETS Research Report Series

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ETS Research Report Series ISSN 2330-8516

RESEARCH REPORT

A Psychological Approach to Human Capital

Harrison J. Kell,1 Steven B. Robbins,1 Rong Su,2 & Meghan Brenneman3

1 Educational Testing Service, Princeton, NJ2 University of Iowa, Iowa City, IA3 The Enrollment Management Association, Princeton, NJ

The concept of human capital originated in economics over 200 years ago. The current paper takes a novel approach to human capitalby interpreting it from a psychological perspective. We divide human capital into 2 domains: traditional and nontraditional. The tra-ditional domain consists of the constructs that have been historically classified as human capital: the cognitive skills and knowledgeassociated with educational success and measured by high-stakes tests. The nontraditional domain consists of constructs that have nothistorically been associated with human capital: personality traits, vocational interests, and psychosocial and academic-related factors.Both traditional and nontraditional human capitals are important predictors of school and work success. Science, however, is concernedwith developing explanations in addition to making accurate predictions, and we move beyond a descriptive taxonomy of human cap-ital constructs by providing a psychological process-based account of human capital grounded in the cognitive–affective processingsystem (CAPS) developed by Mischel and Shoda. To practically illustrate the power of this approach for explaining how human capitalis manifested in individuals’ actions, we offer a CAPS-based model of student help-seeking behavior—a type of behavior that is animportant facilitator of positive educational outcomes.

Keywords Human capital; cognitive skills; personality; predictors of school and work success

doi:10.1002/ets2.12218

Education is seen by many people in the United States as the gateway to the American Dream (Samuel, 2012). To someextent, these beliefs are correct, as educational attainment is associated with a wide variety of positive outcomes, includinglifetime earnings, health, civic engagement, marital satisfaction, and longevity (Barro & Lee, 2001). Despite the Americanemphasis on education, some of the associated statistics are discouraging: Only 30% of Americans attain a 4-year degree(U.S. Census Bureau, 2012), the average 6-year graduation rate is 53% (Carey, 2004), and over 40% of incoming collegestudents fail their first year. These statistics are intrinsically concerning, but are even more so in an age that heavily empha-sizes the knowledge, skills, and abilities often attributed to postsecondary education, which is now virtually a prerequisitefor obtaining many well-paying jobs that have high growth potential and are resistant to outsourcing (Friedman, 2007).For Americans to be able to function effectively in the current working world, both within the United States and abroad,more attention must be given to enhancing educational outcomes for all people (Robbins, Le, & Lauver, 2005).

There are two major perspectives on why some students succeed and others fail. Although the two perspectives arenot necessarily antagonistic, research and practice often tend to focus on one or the other. The first perspective attributesstudent outcomes primarily to influences external to students, such as family, community, social network, and institutional(e.g., school-level) variables. This point of view largely originates in sociology and education and can be characterized asthe social capital perspective (Mouw, 2006; Putnam, 2000; Rothstein & Stolle, 2008; Sanders & Nee, 1996). The secondphilosophy emerged from economics and psychology and prioritizes internal resources in explaining students’ outcomes.This point of view focuses on the characteristics of the students themselves (e.g., interests, knowledge, personality traits,skills) as the determinants of their outcomes, rather than the environments that surround them. It is this human capitalperspective (Becker, 1964; Kiker, 1966; Lubinski, 2000) that this paper addresses.

This paper has several purposes. The first is to articulate a human capital taxonomy that is grounded in psychologicalconstructs. We do this by defining human capital in terms of several well-studied psychological domains. For each domain,we review research evidence supporting its inclusion in the human capital framework by virtue of its association withimportant educational and occupational outcomes. The second purpose is to move beyond a descriptive account of humancapital to an explanatory one. We do this by linking our psychological human capital taxonomy to Mischel and Shoda’s

Corresponding author: H. Kell, E-mail: [email protected]

ETS Research Report No. RR-18-30. © 2018 Educational Testing Service 1

H. J. Kell et al. A Psychological Approach to Human Capital

cognitive–affective processing system (CAPS; Mischel & Shoda, 1995, 2008), a metatheory that enables us to provide aprocess-based account that explains how these domains are manifested at the level of the individual. The final purposeof the paper is to provide a concrete illustration of the practical power of this CAPS-based perspective on human capitalby providing a CAPS-based account of help-seeking, an important type of behavior that is linked to valuable educationaloutcomes. By offering a psychological explanation of a concept that originates in economics, our paper complementsrecent attempts in economics to demonstrate the financial importance of various psychological constructs (e.g., Heckman,2011; Heckman & Kautz, 2012, 2013).

Human Capital Perspective

The human capital orientation focuses on the internal resources that facilitate individuals’ success. Originating in eco-nomics, the concept has deep historical roots (cf. Kiker, 1966). In The Wealth of Nations, Smith (2005) discussed humancapital as

the acquired and useful abilities of all the inhabitants or members of the society. The acquisition of such talents, bythe maintenance of the acquirer during his education, study, or apprenticeship, always costs a real expense, whichis a capital fixed and realized, as it were, in his person. Those talents, as they make a part of his fortune, so do theylikewise that of the society to which he belongs. (p. 227)

Major modern developments of the idea were initiated by Mincer (1958) and Becker (1964), who specified that humancapital consists of the knowledge, skills, and competencies that lead an individual to greater workforce productivity.Schooling is typically considered the most important investment in human capital because through it individuals acquireskills and knowledge that increase their productivity which, in turn, leads to increased earnings over the lifetime, a majorcomponent of extrinsic career success (Judge, Higgins, Thoresen, & Barrick, 1999).

Human capital underscores the economic value of education in its most basic sense: the receipt of systematic instruc-tion. Indeed, emphasizing the economic value of education was an initial impetus for the formulation of the human capitalconcept (Kiker, 1966), long before the advent of compulsory public education. The emergence of the knowledge economyin the latter half of the 20th century (Powell & Snellman, 2004) has only more intimately tied the acquisition of humancapital to institutionalized education, specifically of the postsecondary variety. Entrance into many high-paying, presti-gious occupations (e.g., lawyer, physician, professor) requires credentials that can only be acquired after many years ofschooling. Even outside normatively highly profitable jobs, economic returns to education are large, both at the individual(Hanushek & Woessmann, 2008) and national (Sianesi & Reenen, 2003) levels. Close alignment between the educationaland occupational domains is also evidenced by the fact that many of the psychological variables that predict academicsuccess also predict occupational success (Credé & Kuncel, 2008; Jensen, 1998a; Lubinski & Benbow, 2006; Roberts et al.,2007). This substantial overlap, combined with the necessity of obtaining educational credentials for many (although cer-tainly not all) relatively well-paying, secure jobs in the modern economy, leads us to consider human capital as consistingof psychological attributes that are associated with occupational or educational success, as it is very often the case thatthose same attributes are associated with occupational and educational success.

Major Varieties of Human Capital

We divide human capital into two major types: traditional and nontraditional. Traditional human capital consists of vari-ables that have long been used to predict educational success since the beginning of the 20th century. Many of thesevariables are also associated with vocational success. Nontraditional human capital consists of psychological attributesthat have (re)emerged as predictors of educational and occupational success only in the past few decades.

Traditional Human Capital

The longest-established form of human capital is cognitive skill, or the ability to learn (Snow, 1989, p. 22). The abilityto learn comprises many complex processes and components acting in unison, including induction, deduction, abstrac-tion, and working memory (Carpenter, Just, & Shell, 1990; Carroll, 1993; Snow, 1989; Whitely, 1983). Cognitive skill has

2 ETS Research Report No. RR-18-30. © 2018 Educational Testing Service


been assessed using individual-based (e.g., Stanford-Binet, Wechsler Adult Intelligence Scale [WAIS]) and group-based(e.g., ACT, SAT®) standardized tests since the early 20th century, when those tests were developed to identify childrenwith learning disabilities (Thorndike & Lohman, 1990). Numerous primary studies (Benbow, 1992; Benbow, Lubinski,Shea, & Eftekhari-Sanjani, 2000; Judge et al., 1999; Kell, Lubinski, & Benbow, 2013; Lubinski, Benbow, & Kell, 2014) andmeta-analytic studies (Kuncel & Hezlett, 2007; Kuncel, Hezlett, & Ones, 2004; Ng, Eby, Sorensen, & Feldman, 2005; Rob-bins et al., 2004; Schmidt & Hunter, 1998) demonstrate the sizable association between cognitive skills and importantoutcomes: educational (e.g., college grade point average [GPA], highest degree earned, retention) and occupational (e.g.,creative accomplishments, income, job prestige).

Although there is strong evidence for the presence of a general component of cognitive skill (Jensen, 1998a), narrowercognitive skills tied to specific content areas (e.g., mathematical, verbal, visuospatial) are also important (Carroll, 1993;Major, Johnson, & Bouchard, 2011). The average correlation between scores on tests of specific cognitive skills is .3 (Car-roll, 1993), leaving substantial room for intraindividual variation. Research findings indicate this intraindividual variationhas important practical implications. For instance, individuals with stronger verbal than visuospatial skills tend to pursuedegrees, careers, and creative achievements in the arts and humanities, whereas those with stronger visuospatial than ver-bal skills focus their energies in science, technology, engineering, and mathematics (STEM; Coyle & Pillow, 2008; Coyle,Purcell, Snyder, & Richmond, 2014; Kell, Lubinski, Benbow, & Steiger, 2013; Wai, Lubinski, & Benbow, 2009).

Knowledge acquired through one’s application of ability (i.e., crystallized intelligence; Ackerman, 1996; Cattell, 1943)to learn constitutes a second form of traditional human capital; variables that draw on specific types of content knowledgeare frequently used to predict educational and vocational success. High school GPA is associated with many importantcollege outcomes (Robbins et al., 2004), as are topic-specific Advanced Placement® tests (Morgan & Ramist, 1998) and SATSubject Tests™ (Humphreys, 1986). Scores on measures of this kind are strongly associated with cognitive skills (Bleske-Rechek, Lubinski, & Benbow, 2004). In turn, scores on some of these measures have been directly linked to performanceon the job (e.g., college GPA; Roth, BeVier, Switzer III, & Schippmann, 1996).

Nontraditional Human Capital

The importance of psychological characteristics beyond cognitive skill for educational and occupational success has beenrecognized at least since the imposition of mandatory elementary education in the United States (e.g., Drought, 1938;Flemming, 1932; May, 1923; Pressey, 1920; Rugg, 1920; Spearman, 1927; Webb, 1915). In the first half of the 20th century,a wide variety of noncognitive attributes were investigated as predictors of academic accomplishment, including person-ality (often under the label character traits), interests, motivation, and study habits. For extensive reviews of this earlyliterature, see Harris (1940), Himmelweit (1950), Lord (1950), and Wolf (1938). Indeed, no less than Henry Chauncey,president and founder of Educational Testing Service (ETS), once noted: “Probably little further improvement in the pre-diction of college success can be expected until reasonably valid and reliable measures of such personal qualities havebeen devised” (Chauncey & Frederiksen, 1951, p. 93). Less interested were those in business and government, who wereunsupportive of these measures of noncognitive skill (Lemann, 1999). Around the same time, Guion and Gottier’s (1965)influential review showed minimal associations between personality and job performance. A few years later, Mischel’s(1968) critique led to a decades-long debate about the existence and practical importance of personality traits. Recogni-tion of the practical significance of noncognitive variables generally did not begin to revive until the late 1980s and early1990s (Comer, 1993; Guion, 1987), partially driven by the developing consensus on the Big Five as a unifying person-ality model (Goldberg, 1993). Interest in noncognitive skills has experienced an especially strong resurgence in the last10–15 years (e.g., Duckworth & Seligman, 2005; Heckman, 2011; Heckman & Kautz, 2012, 2013).1

We divide the nontraditional (i.e., noncognitive) human capital realm into three domains. The first two domains arebroad and derived from the major divisions in differential psychology (Lubinski, 2000): personality traits and vocationalinterests. The third domain, relative to personality and interests, is narrow in that it is composed of psychosocial andacademic-related factors (PSFs) that are specifically tied to student performance. We are explicit in aligning traditionalhuman capital with cognitive skills and nontraditional human capital with noncognitive skills, but adding the humancapital label introduces important divisions and subtleties into these domains.

The term, human capital, automatically confers recognition of practical value on the constructs to which it is appliedbecause it is explicitly tied to real-world outcomes (i.e., economic, educational). Thus, a construct may belong to thedomain of cognitive or noncognitive skills but not constitute a form of human capital because its manifestation has a



low probability of yielding positive educational or economic results (e.g., encyclopedic knowledge of medieval witchcrafttreatises); the human capital label is intended to signal real-world relevance, negating the criticism of triviality sometimesleveled at psychological constructs (e.g., Jenkins, 1980; Kline, 1988; Mitchell, 2012). Making the application of the humancapital label contingent on a construct’s reliable association with educational and economic ends also underscores theneed to carefully consider the context in which these constructs—which are sometimes treated as universal (e.g., Jensen,1998a; McCrae & Terracciano, 2005)—are being studied. A construct that counts as human capital in many Westerncountries may not constitute human capital in others, not because of some fundamental cross-cultural difference in theconstruct itself, but simply because it has educational or economic value in one culture, country, or region but not another.Similarly, a body of knowledge and skills not currently educationally or economically valuable could become so in thefuture; proficiency in video games was once considered useless, even harmful (Cravenson, 1982; Newman, 2017), butnow some colleges award scholarships based on it (Diluna, 2017), and millions can be earned in gaming tournaments(Tucker, 2015). By the same token, knowledge and skills that are currently valuable will likely not always be so. For example,the Industrial Revolution rendered hand-weaving skill economically marginal (Hobsbawm, 1963), and many occupationsthat once provided a livable wage have long been all but extinct (e.g., cooper, ice cutter, knocker-up, switchboard operator,tanner, wheelwright). Thus, even if some constructs turn out to be truly invariant across time and place, their status ashuman capital will not be.

Personality Traits

Consensus emerged in the late 1980s and early 1990s that human personality differences can be summarized using fivebroad traits: extraversion, conscientiousness, openness to experience, neuroticism, and agreeableness (Goldberg, 1993).The Big Five have a long past but a short history, as they were first identified by Thurstone (1934), but are identifiable inWebb’s (1915) data, as noted by Deary (1996). Recognition of the theoretical status of the Big Five brought with it therealization of the taxonomy’s practical usefulness for organizing past research findings that attempted to link personalitytraits and real-world outcomes.

As noted previously, at midcentury, the prospect of personality traits being treated as viable predictors of academicperformance was grim. Indeed, in the mid-1960s, the College Board explicitly rejected the use of personality measures forpredicting educational outcomes (College Board, 1963; Kendrick, 1964). After the advent of the Big Five, however, manyempirical studies demonstrated the utility of personality for predicting important educational results (e.g., Furnham,Chamorro-Premuzic, & McDougall, 2002; Higgins, Peterson, Pihl, & Lee, 2007). Poropat’s (2009) massive meta-analysisconfirmed the importance of the Big Five personality traits for predicting academic performance, particularly conscien-tiousness. Importantly, many of the Big Five were shown to be largely independent of cognitive skills.

As late as 1989, it was stated without qualification that “[a]n established tenet within the field of I/O [Indus-trial/Organizational] psychology is that individual personality variables are relatively poor predictors of job performance”(Day & Silverman, 1989, p. 25). Two years later, the situation was entirely different, and the Big Five had been definitivelylinked to job performance through two influential meta-analyses (Barrick & Mount, 1991; Tett, Jackson, & Rothstein,1991). Since then, many primary studies (e.g., Borman, White, & Dorsey, 1995; Borman, White, Pulakos, & Oppler, 1991;Crook et al., 2011; Hough, Eaton, Dunnette, Kamp, & McCloy, 1990; Judge et al., 1999; Kamdar & Van Dyne, 2007; Kell,Motowidlo, Martin, Stotts, & Moreno, 2014) and meta-analyses (e.g., Chiaburu, Oh, Berry, Li, & Gardner, 2011; Huang,Ryan, Zabel, & Palmer, 2014; Hurtz & Donovan, 2000; Ng et al., 2005; Roberts et al., 2007; Salgado, 1997; Schmidt &Hunter, 1998) have linked personality traits to important work-related outcomes, including contextual performanceand organizational citizenship behavior, adaptive performance, income, counterproductive work behavior, number ofpromotions, overall job performance, and job prestige.

Vocational Interests

Career interests (Holland, 1997) are crucial for success in education and in the workforce. Interests serve as the impetusfor individuals to navigate and function effectively in educational and work environments (Su, Rounds, & Armstrong,2009). Mature interest formation is critical to informed career decision-making and to the match between an individ-ual’s measured interests and corresponding education and career choices. In educational contexts, interests function as amotivational force that promotes a love of learning and sustains activities necessary for knowledge acquisition. Interests



promote dedicated and committed action, including graduating high school and pursuing a postsecondary education.Furthermore, interests drive individuals to be engaged and persist in work in such a way that promotes job performanceand career success (Nye, Su, Rounds, & Drasgow, 2012; Su, 2012). Several recent studies highlighted the necessity ofunderstanding the role of interests for academic and career success. Nye et al. (2012) reviewed over 60 studies spanningthe past 70 years and found interests to be important predictors of academic achievement, including GPA and persistencein school, as well as job performance and job tenure. With an analysis of the Project Talent longitudinal data set, a studyon the educational, occupational, and personal developments of over 440,000 students from over 1,300 schools across thenation, Su (2012) showed that interests provide incremental validity over cognitive ability and personality factors for pre-dicting academic performance, degree attainment, and career success, as indicated by occupational prestige and income.In many cases, the contribution of interests is as important as, or more important than, that of personality factors. Intereststurned out to be the strongest predictor of these participants’ income 11 years after their high school graduation, account-ing for four times the amount of variance explained by cognitive ability and personality combined. Congruence betweenvocational interests and educational major is associated with persistence (Le, Robbins, & Westrick, 2014), timely degreeattainment (Allen & Robbins, 2010), and GPA (Tracey & Robbins, 2006). Congruence between vocational interests andoccupation is associated with income and tenure (Neumann, Olitsky, & Robbins, 2009; Strong Jr., 1955).

Psychosocial and Academic-Related Factors

Robbins and colleagues (Casillas et al., 2012; Le, Casillas, Robbins, & Langley, 2005; Robbins et al., 2004) derived 10PSFs (see Table 1) by integrating diverse literatures variously treating motivation, skills, and persistence in models ofeducational success. Although the 10 PSFs loaded on three higher-order factors (motivation and academic skills, socialengagement, self-management), the researchers argued that retaining the narrower dimensions would be most beneficialfor predicting academic outcomes and designing interventions (Robbins, Allen, Casillas, Peterson, & Le, 2006), as aggre-gation of facet-level measures to higher levels of dimensionality can obscure relationships among lower-level constructs(Paunonen, 1998; Paunonen & Ashton, 2001). A subsequent study bore out this conclusion (Peterson, Casillas, & Robbins,2006), as many of the PSFs were shown to be meaningfully related to the Big Five personality traits but predicted collegeGPA better than those broad traits. Subsequent research has demonstrated the 10 PSFs to be associated both directly andindirectly with a wide range of educational outcomes, including success in specific courses, retention, degree attainment,GPA, and use of academic services and resources (Allen, Robbins, Casillas, & Oh, 2008; Allen, Robbins, & Sawyer, 2009;Casillas et al., 2012; Porchea, Allen, Robbins, & Phelps, 2010; Robbins et al., 2006; Robbins, Oh, Le, & Button, 2009).The most consistently impressive predictors of outcomes among the 10 PSFs were specific motivational measures (e.g.,Academic Discipline, Commitment to College), which also evinced associations with educational criteria that were

Table 1 The Ten Psychosocial and Skill Factors

Factor Definition

General determination The extent to which students are dutiful, careful, and dependableAcademic discipline The extent to which students value schoolwork and approach school-related tasks conscientiouslyGoal striving The extent to which students (a) set important goals, (b) make efforts to achieve the goals, and (c) are

confident about their abilities to succeedCommitment to college The extent to which students appreciate the values of education and are committed to attaining the college

degreeStudy skills The extent to which students know how to approach academic-related problems systematically and

effectivelyCommunication skills The extent to which students know how to handle interpersonal problems effectively and can work

cooperatively with others in team or group settingsSocial activity The extent to which students are comfortable in becoming involved in social activitiesSocial connection The extent to which students are involved in the college or school environmentsAcademic self-confidence The extent to which students are confident that they can perform well in schoolEmotional control The extent to which students can effectively control their emotions and keep them from negatively affecting

other activities

Note. Adapted from Le et al. (2005, p. 494).



greater than those observed between general motivational measures (e.g., General Determination, Goal Striving) andthose same criteria.

Tests of the Human Capital Framework

We are not aware of any studies simultaneously examining all aspects of human capital reviewed above (cf. ACT, Inc.,2007). Two studies, however, have investigated the majority of them.

Allen and Robbins (2010) used a longitudinal database comprising 3,072 4-year college students from 15 institutionsand 788 2-year college students from 13 institutions. They found direct effects of interest–major congruence, motivation,cognitive skills, high school GPA, gender, and socioeconomic status on first-year academic performance. They then testedthe direct and indirect effects of these predictors as they worked through first-year academic performance to predict timelydegree attainment. Interest–major congruence was shown to be a direct predictor of timely degree attainment, even aftercontrolling for first-year academic performance, whereas motivation had indirect effects on timely degree attainmentthrough first-year academic performance.

A second study examined the role several human capital elements play in predicting choice of and persistence inSTEM majors (Le et al., 2014). This multilevel longitudinal study tracked the progress of over 200,000 undergraduatesat 51 United States colleges. Students’ vocational interests (Holland, 1997) were assessed, in addition to their cognitiveskill (operationalized by ACT score). It was hypothesized that students whose interests were more congruent with STEMmajors would be more likely to choose and persist in those majors via mechanisms such as motivation, engagement, andsatisfaction. As STEM majors are extremely intellectually demanding (Wai et al., 2009), a synergistic relation was alsoproposed between major–interest congruence and cognitive skill, such that individuals with lower ACT scores wouldbe less likely to choose and persist in STEM majors than individuals with higher ACT scores. The investigation’s majorhypotheses were supported, affirming the need to take into account multiple types of human capital (cf. Lent, Brown, &Hackett, 1994; Lubinski, 2010) to maximize the prediction of educational success.

Mechanisms for Explaining Success

The human capital constructs reviewed are important for forecasting school and work outcomes. However, science callsfor explanation in addition to prediction (Bogen & Woodward, 1988; Magnusson & Torestad, 1993). Indeed, even whileacknowledging the usefulness of the PSFs ATI noted the need for developing an understanding of the psychological mech-anisms that underlie those PSFs, both for expanding basic knowledge and for designing interventions to enhance studentsuccess; similar calls have been made for other constructs included in our human capital taxonomy (e.g., Deary, 2000;McCabe & Fleeson, 2012). We propose that the CAPS (Mischel & Shoda, 1995, 2008) metatheory can answer these calls.CAPS can serve as a viable foundation for linking human capital to practical outcomes, designing interventions, and guid-ing future research. In what follows, we describe the general orientation and components of CAPS and how it can be usedto explain a specific example of effective student behavior: help-seeking.

The Cognitive−Affective Processing System (CAPS) Approach

As noted previously, confidence in the trait approach to personality psychology was at a low ebb from the 1960s until thelate 1980s. Although characteristic of the general zeitgeist of the time, part of this lack of confidence was due to Mischel’s(1968) comprehensive critique, a text which reviewed evidence that cross-situational consistency of behavior is nonzerobut low—too low, it was concluded, to support the dispositional approach to personality as a viable depiction of realhuman beings. Although this was claimed by some to indicate that people had “no personalities” (Goldberg, 1993, p. 26),Mischel (1973) noted that claims such as this represented a misunderstanding of his point, which was that trait approachesdo not accurately portray the complexity of human behavior, nor do they explain it. The CAPS model is an attempt toreconcile low cross-situational consistency of behavior with the belief that human beings nonetheless still have coherent,stable personalities.

CAPS (Mischel & Shoda, 1995, 2008) proposed that people’s behavior2 does vary greatly across situations, but thatthis variability itself is stable. Thus, people’s personalities can be described by reliable if… then … profiles or behavioralsignatures. Even if people respond very differently to different situations, these differences are recurrent and coherent.



Table 2 Major Cognitive–Affective Units

Unit Definition

Encodings Categorizations of the self, people, events, and situationsExpectancies and beliefs Pertaining to the social world, behavioral outcomes in specific situations, and personal

efficacyAffects Feelings, emotions and affective responses (including physiological)Goals and values Desirable or undesirable outcomes and their associated affective states; goals, values,

and life projectsCompetencies and self-regulatory plans Behaviors and scripts that can be enacted; plans and strategies for affecting and

organizing action, behavior, and internal states

Note. Adapted from Mischel (1973); Mischel and Shoda (1995, 2008).

Although the characteristics of these behavioral responses can be aggregated (i.e., treating situational variance as measure-ment error) to form summaries representing people’s standings on abstract traits (e.g., conscientiousness, extraversion),these aggregates can conceal enormous intraindividual differences in how people act in different situations (Fleeson, 2001,2004). For example, even if Person A scores higher than Person B on extraversion overall (i.e., on average), there can stillbe many situations in which Person B behaves in a more extraverted manner than Person A.

CAPS ventures an explanation for these coherent behavioral patterns through cognitive–affective units (CAUs). Asshown in Table 2, these units actively construe, interpret, and react to situational features and themselves interact witheach other to generate resultant behaviors that occur in response to situational demands. The personality system that theseunits comprise is not static but dynamic, uniquely organized in different individuals in ways reflective of their experientialhistories and the functioning of their basic physiological structures. The influence of situations on behavior is posited tobe wholly mediated by this personality system.

Interpreting Human Capital Through the Cognitive−Affective Processing System (CAPS) Approach

Narrow Versus Broad Traits

The person in context approach championed by CAPS fits well with our extended human capital framework and the find-ings that inform it. We suggest that the reason the 10 PSFs are related to the Big Five traits but are also better predictorsof GPA (Peterson et al., 2006) is that broad trait scores conceal considerable within-person variance in behavior acrosssituations. Students who are higher on conscientiousness on average are not necessarily more conscientious at school(i.e., academically disciplined), as there are certainly other areas of life (e.g., extracurricular activities, hobbies, sports)where they can demonstrate the behaviors characteristic of conscientiousness. That is, for some individuals, school-relatedcontexts may activate very different CAUs than nonschool contexts. This interpretation is consistent with findings demon-strating that the intercorrelations of facet-level measures of personality traits leave substantial room for between-personvariance on more narrow dimensions (Dudley, Orvis, Lebiecki, & Cortina, 2006; Trull et al., 1998), that these narrow mea-sures are often more predictive of outcomes than broad measures (Dudley et al., 2006; Tett, Steele, & Beauregard, 2003),and that personality measures specifying a frame of reference (e.g., at school, at work) have higher predictive validitiesthan context-free personality assessments (e.g., Lounsbury, Sundstrom, Loveland, & Gibson, 2002; Shaffer & Postleth-waite, 2012). We view the Big Five traits as summaries of behaviors that occur in a wide variety of contexts—abstractionsbuilt up out of how people think, feel, and act in real-life situations—rather than the causes of those behaviors. In the clas-sic terminology of MacCorquodale and Meehl (1948), this conceptualization treats the Big Five as intervening variablesrather than hypothetical constructs—the end result of the dynamic interplay of many different psychological componentsand processes (Bartholomew, Deary, & Lawn, 2009; Deary, 2000; Horn, 1989; Strelau, 2001; Tryon, 1935; van Der Maaset al., 2006; van Der Maas, Kan, Marsman, & Stevenson, 2017; Whitely, 1983).

Emphasizing specificity over generality is preferable theoretically as well as practically. Recent attention has beendevoted to the fallacies of automatically making inferences about within-person variation based solely on between-personvariation and assuming that the causes of the two are the same (Borsboom, Kievit, Cervone, & Hood, 2009; Borsboom,Mellenbergh, & van Heerden, 2003, 2004; Molenaar, 2004; Voelkle, Brose, Schmiedek, & Lindenberger, 2014). Forexample, Person A and Person B may have identical standing on conscientiousness in general, but the former’s may be



derived from scores on caution and order, whereas the latter’s may be derived from scores on achievement-striving andself-discipline. Further, even if people exhibit the same behaviors in the same situations, the causes of those behaviorsmay be different (i.e., equifinality). Students may demonstrate academic discipline for a wide variety of reasons, includingassociating good grades with getting a good job in the future, impressing peers, or parental expectations, and these dif-ferences can be represented by differences in the nature and organization of their CAUs. More narrow dimensions, eitherdue to contextual or behavioral specificity, lend themselves more readily to explanation via psychological mechanisms,as the number of their likely causes is more limited than that of broad dimensions, which partake of many differentbehaviors over many different contexts. Indeed, it is at the level of specific behaviors, rather than abstract dispositions,that interventions aimed at changing personality are targeted and more likely to be successful (Hudson & Roberts, 2014;Magidson, Roberts, Collado-Rodriguez, & Lejuez, 2014; Roberts, Lejuez, Krueger, Richards, & Hill, 2014).

Human Beings as Motivated Agents

Traditional trait approaches have been criticized not only for not offering explanations for behavior, but also for portrayingpeople largely as automata, essentially hosts for psychological variables that automatically respond to different situationsand stimuli in accordance with their preprogrammed trait standings (Bandura, 2006; Mischel, 1973). McCrae and Costa(1995) objected to aspects of social–cognitive theory on the very grounds that much of human behavior is not goaldirected, intentional, or otherwise motivated. We reject these views of human beings as unrealistic and unhelpful. Wefavor an overall approach that can be described as agentic,3 one that views people as volitional, purposeful, proactive,engaged, motivated, and exhibiting self-control and willpower. Agents proactively interact with, adjust to, and shape theirenvironments to reach desired outcomes. The underlying assumption of the agentic perspective is that people can shapeand influence the world around them and that each individual has the capacity to accumulate knowledge and skills, gathersocial and interpersonal resources, and pursue opportunities that will increase the likelihood of his or her success in life.We do not deny the reality of human variability and the fact that some can gather more of these resources or gather themmore quickly or efficiently—but that does not change our contention that the normal population of the United States iscapable of being successful (Bickel & Beaujean, 2005; Robbins et al., 2005). The agentic perspective is “potentialist” in thatit emphasizes what people are capable of becoming (Allport, 1955; Bandura, 2006)—what people do rather than whatthey have or are (Cantor, 1990).

Behaviors that are typically described as agentic include creating and taking advantage of opportunities, risk-taking,assertiveness in protecting one’s rights and pursuing one’s goals, persistence in goal pursuits, and changing one’s situationto achieve a better fit with interests (Sadri, 1996). Agency is positively correlated with extraversion and conscientiousnessand negatively correlated with neuroticism (Markey, 2002). These correlations are only moderate, however, and of similarmagnitude to the correlations observed between the 10 PSFs and the Big Five traits. We suggest that agency must beconsidered in context and that different situations activate different CAUs in different people: The assertive, achievement-striving straight-A student may be nervous and lost on the sports field where the star athlete excels, but the psychologicalroles may be very much reversed in the classroom. Belief in one’s skills is partially grounded in the objective reality of thoseskills and past performance (Lubinski, 2010; White, 1959)—but that relationship is far from unity (Robbins et al., 2004).

Research supports the viability of the agentic perspective. Individuals actively seek out situations that are compatiblewith their characteristics and select them (Ickes, Snyder, & Garcia, 1997), and this consistency in choosing situations ispartially responsible for the consistency of personality over long periods of time, as evidenced by the fact that heritabilityincreases over time, largely as a function of the extent to which people have more freedom to choose their environmentsas they grow older (Scarr, 1996; Scarr & McCartney, 1983). Even when people find and choose surroundings amenable totheir personal characteristics, they often further shape those surroundings to suit their needs, to the point that personalitycan be accurately inferred from bedrooms, offices, and websites (Gosling, Ko, Mannarelli, & Morris, 2002; Vazire &Gosling, 2004).

Robbins et al. (2009) demonstrated the importance of the agentic perspective in their meta-analytic path analysis ofthe effects of various interventions on college performance and retention. They tied the three major PSF types (motivationand skills, self-regulation and management, social engagement) to three psychological control mechanisms featured in themotivation/self-regulation, skill acquisition, and training literatures (Bell & Kozlowski, 2008; Kanfer & Ackerman, 1989;Kanfer & Heggestad, 1997; Kozlowski & Bell, 2006): motivational control, emotional control, and social control. Theresults of their analyses indicated that interventions targeting these three constructs have a positive, practical impact on



college outcomes, thus supporting the viability of agentic mechanisms (e.g., discipline/self-control, motivation, willpower)as psychological explanations for the correlation between PSFs and academic success.

To further strengthen our case for our agentic perspective, we tie it directly to the Bandura (2001, 2006) model of humanagency. This framework underscores the importance of intentionality (i.e., cognition), willpower (i.e., self-control), andtheir interplay. The core of human agency, however, is the perception of efficacy, based on the simple principle that indi-viduals are unlikely to carry out actions if they do not believe those actions will bring about the fulfillment of their desiredgoals. Although efficacy beliefs are partially founded on true skills (Lubinski, 2010; White, 1959), they possess incremen-tal validity beyond those objectively assessed skills for predicting outcomes (Robbins et al., 2004). Thus, even if peoplehave the skills needed to accomplish a task, if they do not believe they have those skills, they will not be motivated to evenattempt the task or persist in it if they run into difficulties. Without adequate motivation and effort, even great skill will notbe enough to yield adequate performance outcomes (Campbell, 1990), as has been found in studies of performance on low-stakes cognitive tests (Duckworth, Quinn, Lynam, Loeber, & Stouthamer-Loeber, 2011; Liu, Bridgeman, & Adler, 2012).

Critically, self-efficacy is held to be task or domain specific. Just like narrow trait measures, self-efficacy beliefs aboutmany different areas can be aggregated to form a global index—but this index will obscure crucial cross-domain variationin those self-perceptions. Because people tend to select into situations and domains that are best suited to their strengths(Kell et al., 2013; Wai et al., 2009) and that they find engaging and intrinsically motivating (Le et al., 2014), treating self-efficacy globally may inaccurately portray individuals as less capable than they are in reality, by lumping in their beliefsabout their skills in domains that they actively avoid with their perceptions about their skills in domains they activelypursue. As with personality measurement, this approach can have negative consequences for both prediction and expla-nation. If being a monomaniac in pursuit of excellence in a single area is truly a major key to success (Simonton, 1994),then it would be best to focus on the assessment of individuals’ strengths (subjective and objective) in that single arearather than placing heavy weight on strengths in areas partially or wholly irrelevant to them.

Integrating Perspectives

CAPS is intended to be a comprehensive framework that can be used to organize and integrate research findings in diverseareas, facilitating cumulative progress in scientific psychology (Miller, Shoda, & Hurley, 1996; Mischel & Shoda, 1995,2008). We believe it can serve just this purpose in the student achievement domain.

Examination of Table 3 indicates substantial overlap of the major PSFs, control mechanisms, and the four compo-nents of the human agency model. Although these constructs differ in their nuances, we propose that they are describingthe same explanatory mechanisms and that these mechanisms constitute specific examples of the broad CAUs that formthe core of the CAPS personality system (compare the descriptions in Tables 2 and 3). We make the following rational(Nunnally & Bernstein, 1994) categorizations: First, the PSFs and control mechanisms are equivalent (cf. Robbins et al.,2009). Second, social engagement–social control is an example of intentionality, as it constitutes a type of action strat-egy. Third, motivational control–motivation and academic skills and emotional control–self-management are examples ofself-reactiveness, as they are active, online processes that control and regulate behavioral strategies adopted when pursu-ing goals. Fourth, because the competencies and self-regulatory plans CAU includes both the regulation of behavioralstrategies and the strategies themselves, it encompasses intentionality, which involves action plans, and self-reactiveness,which involves the control of those plans. Fifth, self-reflectiveness is part of the expectancies and beliefs CAU, as it refers tobeliefs about personal efficacy. Finally, forethought is part of the goals and values CAU, as both concern goals. A graphicalsummary of this categorization is depicted in Figure 1.4

Applying CAPS for Understanding

Conceptualizing PSFs associated with student success as CAUs continues the theoretical integration initiated by Rob-bins et al. (2004). Because CAPS provides a guide for developing detailed domain-specific models, it also constitutes anopportunity for moving beyond observing correlations between PSFs and student achievement to venturing explanationsfor why those associations exist. We illustrate the promise of linking PSFs to CAPS by describing a CAPS-based analy-sis of an important behavioral domain among students: help-seeking. Among the myriad important types of behaviorsthat students engage in (e.g., paying attention to lessons, studying, teamwork) we have chosen help-seeking because itis philosophically aligned with our conception of human beings as motivated agents. Students who engage in effective



Table 3 Definitions of Major Psychosocial and Skill Factors, Control Mechanisms, and Components of the Human Agency Model

Unit Term Definition

Components ofHuman Agencya

Intentionality Deliberate and purposeful action plans and strategies (some of which may entailinteractions with other people); cognitive representations of future course ofaction to be performed

Forethought Goals and cognitive constructions of how they can be reached through the selectionor creation of various courses of action (or the avoidance of actions likelyundermine reaching those goals)

Self-reactiveness Motivating, enacting, monitoring, and regulating behavioral patterns (often throughsetting proximal goals); comparison of current state and with desired goal state;adopting more challenging goals as desired proximal goals are achieved

Self-reflectiveness Beliefs about personal efficacy: the degree to which people believe their actions willbring about their desired outcomes

Control Mechanismsb Motivational control Self-regulatory processes through which individuals’ ability to implement trainingactivities are enhanced

Emotional control Processes through which individuals self-manage attitudes and feelings directlyaffecting their receptiveness to and implementation of training activities

Social control Processes through which individuals engage their social environment(s) tofacilitate, support, and reinforce their learning activities

Psychosocial andSkills Factorsc

Motivation andacademic skills

Processes that drive student engagement with, pursuit of, and proficiency inacademic-related behaviors

Self-management Processes facilitating academic goal development and achievement throughself-control, discipline, and confidence in academic skills

Social engagement Processes facilitating students’ gaining social support from and involvement withother individuals in their school communities

aBandura, 2001, 2006 bKanfer and Heggestad (1997), Kanfer and Heggestad (1999); Robbins et al. (2004); Robbins et al. (2009). cLeet al. (2005); Robbins et al. (2009).

Figure 1 Results of rational categorization of constructs comprising cognitive−affective processing systems, human agency theory,psychosocial and skills factors, and control mechanisms.

help-seeking behavior are proactively shaping their environments in addition to adjusting to their circumstances (i.e.,being challenged in some way). Students who seek help are taking control of themselves and their situation by workingto address their difficulties in an empowered, motivated way.

Help-seeking is an important learning and self-regulation strategy employed by students and is associated with positiveeducational outcomes (Ames, 1983; Karabenick & Dembo, 2011; Karabenick & Knapp, 1991). Despite this, when con-fronting learning difficulties or expecting unsatisfactory academic performance, many students do not seek help (Butler,1998; Karabenick & Knapp, 1991). There are many reasons a student may choose not to seek help, ranging from fear of



appearing incompetent, potential embarrassment, or adherence to a credo of stoic self-reliance (Butler, 1998; Karabenick& Dembo, 2011) to perceptions that peers or teachers will not provide help even if a request is put forth (Karabenick, 2004).

Among those students who do seek help, qualitative differences exist in the strategies they employ. Although severaltypologies of help-seeking strategies exist (e.g., Butler, 1998; Karabenick, 2004; Ryan, Patrick, & Shim, 2005), theirconstituent behaviors can be divided into two major categories. The first category is regarded as adaptive because itsprings from a desire to increase understanding and leads to the reduced need for assistance in the future; this category isoften labeled instrumental (Butler, 1998) or autonomous (Karabenick & Dembo, 2011). The second category is regarded asmaladaptive because it revolves around short-term solutions to the current problem (e.g., copying or asking for answers)that will not lead to increased autonomy in the future but rather to continued dependence; frequent labels for this typeof help-seeking include avoidant (Karabenick, 2004), expedient (Butler, 1998), and covert (Newman, 1990). Maladaptivehelp-seeking behavior is often due to concerns about one’s ability, self-esteem being threatened by admitting that help isneeded, or appearing incompetent to others (Butler, 1998; Fisher, Nadler, & Whitcher-Alagna, 1982; Karabenick, 2003,2004; Nadler & Fisher, 1986). For both students who do not seek help and students who do so in maladaptive ways, theperceived costs to self-esteem of seeking help often outweigh perceived benefits (Butler, 1998; Karabenick & Knapp, 1991;Newman, 1990).

CAPS-based models have been applied to such diverse phenomena as ethnic relations (Mendoza-Denton & Goldman-Flythe, 2009), cognitive behavioral therapy (Shoda & Smith, 2005), interpersonal relationships (Zayas, Shoda, & Ayduk,2002), reactions to the verdict of the O. J. Simpson trial (Mendoza-Denton, Ayduk, Shoda, & Mischel, 1997), and breastcancer prevention and treatment (Shaw et al., 2008). Full-fledged CAPS models constitute entire academic papers, andspace limitations prevent us from developing one here. Nonetheless, we believe even an outline of a CAPS analysisdemonstrates the utility of the approach. Consequently, in Figure 2 we present a sketch of a CAPS-based analysis of thedeterminants of a hypothetical high school student’s (Julian) instrumental help-seeking behavior when confronted withan upcoming calculus test. We expand on several particularly important aspects of this sketch:

• Although we have outlined the activation of the CAUs in sequential, static steps, this is an oversimplification of whatlikely occurs in real life; CAUs are presumed to be dynamically related to each other (Mischel & Shoda, 1995, 2008).We also do not presume that all the cognitions detailed are necessarily conscious for all individuals (Karabenick &Dembo, 2011; Mischel & Shoda, 1995, 2008).

• Relevant theoretical findings are easily integrated using the CAPS approach. For example, the fact that mastery goalsare related to instrumental help-seeking (Karabenick, 2004) is featured pervasively throughout Figure 2 (e.g., Steps7, 8, 10, and 12), along with the identification of certain CAUs as instances of specific PSFs/control mechanisms(Steps 7, 14, 15, and 16).

• The cognitive–affective psychological process is initiated by the encoding of the situational feature of the upcomingcalculus test and determined by perceiving this test as a meaningful event worthy of further attention. If the testwas not perceived as being personally important, the entire subsequent sequence of activations would not occurand no help-seeking would have taken place. This underscores the fact that encodings of situational features are thefirst CAUs to be activated and arguably the most important for determining behavior (Mischel, Mendoza-Denton,& Hong, 2009).

• Even small changes to the CAUs activated (or their interrelations) could realistically result in a cascading effectresulting in no help being sought (i.e., encoding) or a maladaptive strategy being adopted. For instance, if Julian wasmore concerned about appearing incompetent in front of his peers (a performance goal) than lacking the necessaryunderstanding of calculus, he may have been unwilling to put in the effort needed to develop this understanding,leading him to ask Jane for her notes and homework assignments rather than fundamentally developing his knowl-edge of calculus itself (expedient help-seeking). By the same token, if Julian did not believe Jane (a) had a firm graspof calculus, (b) was good at explaining things, and (c) was willing to help him, he may never have employed the PSFof social engagement.

• It is critical to emphasize that the analysis applies to the single student (Julian) and may not necessarily generalizeto any other student. The social−cognitive approach to personality theory maintains that, for the full complexityof real human beings to even begin to be understood, analysis must be conducted at the level of the individual andin the context of specific situations (Cervone & Shoda, 1999). The hypothetical analysis for Julian indicates that hisif . .. then profile for when he is faced with an important academic event for which he feels unprepared is to seek



Figure 2 Outline of plausible psychological processes that lead to adaptive help-seeking behavior based on the principles of thecognitive–affective processing system (CAPS). The relevant cognitive–affective unit (CAU) is presented in parentheses at each step.The psychosocial and skills factor or factors (PSFs) and control mechanisms the CAU represents are also indicated, when relevant. Forthe purposes of this example, we assume that all of Julian’s perceptions (of his need to better understand calculus to do well on the test,of the teacher being unwilling to help, of Jane being willing to help) are accurate.

help in an instrumental fashion; the results of this analysis do not guarantee he will seek help in response to all (oreven most) situational features. Developing a fuller understanding of individuals requires intensive, repeated studyof their behaviors, and the cognitive−affective units that may underlie them, across a diverse set of situations.

Our rudimentary analysis both demonstrates the feasibility of using CAPS as an overarching framework to betterunderstand the relations of PSFs to behaviors facilitative of student success and underscores the complexity and diversityof the processes leading up to those behaviors—and their fragility. As noted, even slight changes to the CAUs activatedin Figure 2 could have resulted in maladaptive behavior. Nonetheless, despite the potential explanatory power of CAPSmodels, the fact that they are focused on single individuals inherently limits the generalizability of predictions basedon them, as opposed to less psychologically accurate but more practically powerful models based on large aggregates ofindividuals. This trade-off, coupled with the ever-present problem of scarce resources, may cause some to believe that theapparent drawbacks of CAPS-based models outweigh their benefits. Clearly, we do not believe that is the case, and muchof that belief is based on the practical utility of CAPS for designing effective interventions to improve human well-being.



Applying CAPS for Intervention

The results of a thorough analysis of the CAUs underlying differences in the behavioral pattern of interest essentiallydictate the psychological target(s) of an intervention aimed at behavioral change. In terms of practical implications, theusefulness of CAPS lies in its ability to uncover the fine-grained determinants of individual differences in human behav-ior. Further, although CAPS analyses aimed at developing comprehensive models of specific people necessarily entailextremely thorough and intensive study at the individual level, a CAPS approach to intervention can instead focus ongroups of individuals that reliably differ on the behavior of interest (i.e., the contrasting groups approach; Cronbach &Meehl, 1955; Humphreys, Lubinski, & Yao, 1993). Initially, at a fairly molar level, specific CAUs that also differentiate thetwo groups can be identified. For example, as previously mentioned, mastery goals often accompany instrumental help-seeking, and performance−approach or performance−avoid goals often accompany expedient help-seeking (Karabenick,2004). If an intervention is being designed to, for example, help undergraduates in a biology class who are currently expe-dient help-seekers become instrumental help-seekers, a first step can be to verify that the two groups of help-seekers inthat class reliably differ in their achievement goals. If that is indeed the case, interventions can be designed to attemptto change the goals of the maladaptive help-seekers, encouraging students to develop a thorough understanding of thematerial, rather than simply taking shortcuts that will help them do well on tests. A possible mechanism for changingachievement goals is reframing (Miller et al., 1996), which consists of emphasizing different aspects of the subject mat-ter so that students may become more intrinsically interested in it, encouraging a shift from a learning strategy focusedsimply on doing well on tests to one focused on acquiring a fundamental understanding of the material. The extent towhich such an intervention was effective in shifting some students’ help-seeking from expedient to instrumental wouldconstitute some evidence for the influence of achievement goals on help-seeking.

Even if the proposed intervention had wide-ranging effects, it would likely not facilitate the desired behavioral changein all the students to whom it was applied. This would trigger a second round of CAPS-based analyses, this time basedon a different CAU. The next step might be to examine self-efficacy for the subject matter (beliefs and expectancies)among students for whom the goal-based intervention was effective and among those for whom it was not. If self-efficacydifferences were discovered that differentiate the two contrasting groups, an intervention targeting this CAU could bedesigned, implemented, and its results assessed. This iterative process of identifying CAUs that reliably differentiate con-trasting groups, attempting to change them via intervention, assessing results, and then targeting successively more selectsubgroups could continue until some acceptable degree of behavioral change is reached among the students or practicaland economic resources are depleted to the point that no further interventions can be designed or implemented.

It is important that interventions based on CAPS adhere to the following criteria. First, they must be domain-specific.In the preceding example, the domain of interest was tightly specified: help-seeking behavior in an undergraduate biol-ogy class, not help-seeking in general (i.e., a trait). Because different situations present different situational cues that thenlead to manifestations of different if … then… profiles, this approach does not assume that students will pursue thesame help-seeking strategy in every class, let alone throughout all aspects of their lives. Specific interventions tend to bemore effective than general ones in spurring behavioral change (Hudson & Roberts, 2014; Magidson et al., 2014; Robertset al., 2014). Second, the recommended intervention is tailored to groups that are homogeneous in their standing on theconstruct of interest, rather than fully tailored to specific individuals. As noted, a potential objection to CAPS-based anal-yses is that they are idiographic, not generalizable, and thus impractical. We acknowledge that designing interventionsbased on CAPS will indeed be resource intensive, but that, once formulated, those interventions can be generalized to theindividuals who fall into the specified group. Finally, depending upon practical goals and resources, CAPS-based inter-ventions can be designed according to a top-down or bottom-up approach (Mischel et al., 2009). The bottom-up strategyis the most comprehensive and detailed: Full-fledged CAPS-based analyses are conducted of all individuals who differ inthe behavioral patterns of interest. All the CAUs related to this behavioral domain are identified and their interrelationsto each other are mapped at the individual level. Interventions can then be tailored solely to individuals and the idio-graphic organization of their CAUs (the most resource-intensive approach) or to groups of individuals whose CAUs aresimilarly organized (a somewhat less resource-intensive approach). The bottom-up approach is largely atheoretical, makesno a priori assumptions about which CAUs are likely to influence behavior, and is focused on discovering (Reichenbach,1938) and exploring (Tukey, 1969, 1977) the relevant CAUs before formulating intervention plans based on those findings.When time and material resources are scarce (as they usually are), a more limited, top-down approach can be employed.Here, only the CAUs identified by prior research that are frequently related to the behavior(s) of interest are assessed.



Interventions for the latent classes identified can be developed de novo, or intervention techniques that have been provento be effective in the past can instead be implemented. By taking a theoretically driven approach informed by cumulativeresearch findings, the top-down strategy makes explicit a priori assumptions about which CAUs are most important forinfluencing behavior and, by implementing interventions to affect those CAUs, attempts to obtain empirical evidence thatconfirms and justifies the theories on which they are based (Reichenbach, 1938; Tukey, 1969, 1977).

Conclusion

We have reviewed evidence for the importance of PSFs as predictors of student success, made a case for these PSFs beingelements of the CAPS personality system, and demonstrated the theoretical and practical implications of the PSF–CAPSlink for facilitating greater understanding of students’ academic success behaviors and designing interventions to enhancethose behaviors. By linking PSFs to CAPS, we emphasize the importance of specificity over generality and call for researchand intervention methods tailored to individual students to the greatest extent possible. We are far from the first toadvocate individualized treatment of students—psychologists with such diametrically opposing viewpoints as HowardGardner and Arthur Jensen have called for adapting educational methods to suit the needs of individual students tomaximize their academic success (Gardner, 2009; Jensen, 1998b).

Although limited resources will always pose difficulties, now is an opportune time to turn our attention to tying researchand practice to students’ individuality. In the past 15 years, sophisticated methodologists have criticized psychology formaking inappropriate inferences about individuals from group-level data (e.g., Borsboom et al., 2004; Voelkle et al., 2014)and called for a renewed emphasis on person-centered research (Molenaar, 2004; Molenaar & Campbell, 2009; Sterba &Bauer, 2010). Recent research using modern statistical methods into the phenomenon of aptitude−treatment interactions(ATIs), so often the domain of weak or null results (Corno et al., 2002), has revealed evidence for reliable interactionsbetween student subgroups and instructional methods (Fuchs et al., 2014). More fundamentally, findings from the fieldof behavioral epigenetics are challenging long-held beliefs about person−environment transactions at the biological level,with enormous implications for our understanding of human development (Lester et al., 2011; Miller, 2010; van IJzen-doorn, Bakermans-Kranenburg, & Ebstein, 2011). With this convergence of theory, method, and research findings bothinside and outside of psychology, now is the opportune time to refocus the study (and improvement) of student successaccording to the principles we advocate.

Notes

1 Rejection of noncognitive predictors may also have been due to the fact that, in the decades following World War II, externalexplanations for human behavior were preferred over internal ones (Gillette, 2007; Kimble, 1993; Scarr & Weinberg, 1977).During this period, differences in performance on measures of cognitive skill were considered by many to be attributable tocultural and socioeconomic bias rather than variation in substantive psychological variables (Jensen, 1980; Kamin, 1974;McClelland, 1973).

2 CAPS is about describing and understanding the determinants of behavior. CAPS arose from the social–cognitive (Cervone &Shoda, 1999), rather than the differential (Lubinski, 2000), tradition in psychology, and it does not make strict distinctions amongcognitive skills, personality traits, and interests. Instead, CAPS strives to describe and understand the “whole person” or “totalpersonality” (cf. Allport, 1924; Eysenck & Eysenck, 1985; Fromm & Hartman, 1955; Magnusson, 1999; Vernon, 1935; Wechsler,1950), rather than breaking human beings down into prespecified measurement domains and studying them in isolation.

3 We have chosen the generic label of agentic perspective due to the great diversity of approaches all treating the same basic idea ofhuman beings’ capacity to intentionally carry out actions in the pursuit of goals. Although we draw on work that is oftenconsidered social−cognitive in the psychological sciences, simply labeling this viewpoint a “social–cognitive perspective onhuman agency” would deny its breadth: Characterizations of human agency date back at least to Aristotle (Williams, 1992) andare found in many disciplines outside psychology, including philosophy (Berlin, 1990; Davidson, 2001), human development(Bronfenbrenner & Morris, 2006; Elder, 1994), and economics (Sen, 1985). Inside psychology, agentic principles have beenbroadly invoked for over a century, including by functionalists such as William James and John Dewey (Hilgard, 1987), learningtheorists such as Woodworth (Woodworth & Marquis, 1948), and personality theorists such as Gordon Allport and HenryMurray (Allport, 1955; Mischel & Shoda, 1995). More recently the same theme has been treated as a part of positive psychology(Seligman & Csikszentmihalyi, 2000), cognitive psychology (Cantor, 1990), self-determination theory (Ryan & Deci, 2000), and



giftedness (Lubinski & Benbow, 2000). Through its inclusion of motivation and engagement, agency also plays a pervasive role intheories of fit (Dawis & Lofquist, 1984; Schneider, 1987).

4 A careful reading of the descriptions of the constructs in Tables 2 and 3 reveals our categorizations are not perfect and likely donot hold for all individuals at all times. For example, some students may use social engagement/control processes as a means ofregulating their behavioral patterns (self-reactiveness). Likewise, as forethought comprises not only goals but also cognitiverepresentations of how goals can be reached, it could be argued that it is split between the goals and values and competencies andself-regulatory plans (CAUs). Part of the difficulty is due to the fact that the boundaries of the CAUs themselves are somewhatindistinct. For instance, setting obtainable objectives (i.e., goals & values) is often promoted (Bandura, 2001, 2006) as a means ofcontrolling behavior (competencies & self-regulatory plans). As psychology lacks well-developed theories (Borsboom, 2005),however, open concepts with fuzzy boundaries are the norm, not the exception (Meehl, 1978, 1990). We believe our classificationscheme is useful because it explicitly recognizes the recurrence of similar psychological concepts across diverse areas of inquiry(cf. Lubinski & Benbow, 2000), which is a testament to their likely importance for explaining human behavior.

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Suggested citation:

Kell, H. J., Robbins, S. B., Su, R., & Brenneman, M. (2018). A psychological approach to human capital (ResearchReport No. RR-18-30). Princeton, NJ: Educational Testing Service. https://doi.org/10.1002/ets2.12218

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