a research note on the incremental validity of job knowledge and integrity tests for predicting...
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A Research Note on theIncremental Validity of JobKnowledge and IntegrityTests for Predicting MaximalPerformanceDeniz S. Ones a & Chockalingam Viswesvaran ba University of Minnesotab Florida International UniversityPublished online: 05 Dec 2007.
To cite this article: Deniz S. Ones & Chockalingam Viswesvaran (2007) A ResearchNote on the Incremental Validity of Job Knowledge and Integrity Tests forPredicting Maximal Performance, Human Performance, 20:3, 293-303, DOI:10.1080/08959280701333461
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A Research Note on the IncrementalValidity of Job Knowledge and Integrity
Tests for Predicting MaximalPerformance
Deniz S. OnesUniversity of Minnesota
Chockalingam ViswesvaranFlorida International University
In a sample of industrial job applicants, relationships among scores on an integritytest, a job knowledge measure, and maximal performance as assessed by a work sam-ple measure were investigated. The observed correlation between the personal-ity-based integrity test and maximal performance was .27, indicating that integritytests can be predictive of maximal performance. Furthermore, integrity test scorescorrelated .14 with job knowledge and job knowledge scores had a validity of .36 formaximal performance. Theoretical implications for the maximal/typical perfor-mance distinction are discussed.
This study examines the relationship between job knowledge measures and integ-rity tests in predicting maximal work performance. Our goal is to present data thatsupport the notion that integrity tests can predict maximal performance, in additionto their previously meta-analytically established predictive validity for typical per-formance (Ones, Viswesvaran, & Schmidt, 1993). We also aim to show that integ-rity tests have incremental validity over cognitively loaded job knowledge mea-sures in the prediction of maximal performance. In the following paragraphs, wefirst highlight the conceptual relations and distinctions between typical and maxi-mal performance, making a case for the importance of investigating maximal per-formance. We next focus on work samples as measures of maximal performance.
HUMAN PERFORMANCE, 20(3), 293–303Copyright © 2007, Lawrence Erlbaum Associates, Inc.
Correspondence should be sent to Deniz S. Ones, Department of Psychology, University of Minne-sota, 75 East River Road, Minneapolis, MN 55455–0344. E-mail: [email protected]
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We then review the conceptual bases for studying integrity and job knowledge asdeterminants of work sample performance and offer specific hypotheses.
MAXIMAL PERFORMANCE:CONCEPTUAL BASIS AND IMPORTANCE
In recent years, personnel selection researchers have distinguished between typicaland maximal performance (see Sackett, Zedeck, & Fogli, 1988, for the introduc-tion of the concepts to the domain of criterion measurement and Klehe & Ander-son, 2005, for a summary of recent research). Typical performance refers to theday-to-day performance on the job over extended periods of time and comprises ofsituations where the performers are not continually being monitored and are notconsciously performing at their optimal level of performance (Sackett et al., 1988).On the other hand, employees perform maximally when they are continually moni-tored, bear the consequences of their performance, and therefore consciously try toperform at their peak capacity. Typical performance refers to what people will do,and maximal performance refers to what people can do. Thus, typical performanceand maximal performance are theoretically distinct.
Conceptually, maximal performance sets the ceiling on job performance. AsDuBois, Sackett, Zedeck, and Fogli (1993) noted, the distinction between typicaland maximal performance lies in the role that motivation plays in each. Typicalperformance differs from maximal performance due to greater variability in moti-vation among employees when they have choices in three motivational elements:to perform or not to perform, the level of effort to expend, and to persist or not topersist. Conversely, it is possible to view maximal performance as a motivationallycharged-up version of typical performance, whereby external monitoring and con-sequences act to equalize motivational differences among employees. Either way,ability to perform maximally is a necessary but not a sufficient precursor to typicalperformance.
In support of the theoretical distinctions between maximal and typical perfor-mance, empirical studies report low to moderate correlations between the twotypes of measures. For example, Sackett et al. (1988) found correlations rangingfrom .11 to .32 between the two types of measures in a sample of 1,370 supermar-ket cashiers. Similarly, Klehe and Latham (2003) found that typical and maximalperformance correlated .30. Findings such as these raise the need for validatingpredictors with both types of measures (or clearly delineating what criteria will bepredicted by a particular measure). Predictors may have differential usefulness formaximal and typical criteria, and investigations with each are essential in provid-ing a full understanding of job performance. Describing and explaining how agiven predictor is linked to job performance is essential for building models ofperformance.
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Work Sample Tests as Measures of Maximal Performance
Previous fieldwork on typical performance has operationalized the construct asoverall job performance or unmonitored task performance over an extended pe-riod. Such an operationalization is consistent with the conceptualization of typicalperformance as motivationally less constrained, average daily performance. In as-sessing maximal performance, there are three key features: (a) awareness amongassessees that they are being monitored, (b) “receipt and acceptance of instructionsto focus full attention on optimal performance” (DuBois et al., 1993, p. 206), and(c) limited duration such that the assessee can maintain a high level of effort(DuBois et al., 1993). Previous field research has utilized standardizedwork-sample tests to assess maximal performance (e.g., DuBois et al., 1993;Sackett et al., 1988). Work sample tests conform to all three required features ofmaximal performance measures. First, individuals are aware that they are beingmonitored and evaluated on their performance during the work sample test. Sec-ond, because work sample tests are typically administered for selection or promo-tion decisions, assessees focus their attention on performing at their peak. Third,their duration ranges from a few minutes to several hours, enabling a high level ofeffort among those being evaluated throughout the testing period. Thus, work sam-ple tests provide conceptually high-fidelity measurements of maximal perfor-mance. In this research, we use work sample tests as measures of maximalperformance.
Cognitive and Noncognitive Antecedentsof Maximal Performance
Campbell (1990) noted that all job relevant behavior is determined jointly by cog-nitive and noncognitive factors. Maximal and typical performances are no excep-tions. The difference between the two lies in the relative contributions of cognitiveand noncognitive determinants to the type of performance at hand. Conventionalwisdom in personnel selection suggests that ability-loaded predictors such as cog-nitive-ability tests and job knowledge measures should predict maximal perfor-mance particularly well (Cronbach, 1960), and there is vast empirical evidencethat they do (e.g., Campbell & Knapp, 2001; DuBois et al., 1993; Schmidt, Hunter,& Outerbridge, 1986). On the other hand, noncognitive measures such as personal-ity scales should have greater value in predicting typical performance because oftheir closer links to motivational variables (McCloy, Campbell, & Cudeck, 1994;Ones, Viswesvaran, & Dilchert, 2005b). The unfortunate assumption is thatnoncognitive measures should have very limited usefulness in predicting measuresof maximal performance. In this research note, we provide an examination of thisassumption. More specifically, we concurrently examine and compare relation-ships of a cognitively based measure (job knowledge) and a noncognitive measure
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(integrity) with a measure of maximal performance. Previous empirical studies ofmaximal performance antecedents have used either cognitive predictors (e.g., abil-ity, job knowledge; DuBois et al., 1993) or noncognitive predictors (e.g., personal-ity; Kirk & Brown, 2003; Ployhart, Lim, & Chan, 2001), but not both. Further-more, previous examinations of noncognitive predictors of maximal performancehave been limited to the Big Five (Ployhart et al., 2001) and motivational traits(self efficacy, achievement motivation; Kirk & Brown, 2003). An examination ofthe integrity–maximal performance link expands the nomological net for bothconstructs.
Integrity and maximal performance. Why should integrity be related tomaximal performance? To answer this question, we first describe the theoreticalunderpinnings of the integrity construct and then provide a theoretical rationaleconnecting it to maximal performance. Integrity refers to the honesty and trustwor-thiness of individuals. Scores on integrity tests tap into a higher order factor of per-sonality termed “alpha” by Digman (1997). Factor alpha represents a super-ordinate grouping on conscientiousness, agreeableness, and emotional stability(the same set of traits assessed by integrity tests; Ones, 1993) and has been empiri-cally supported in both large-scale primary studies and meta-analytic investiga-tions of personality factor structure (Ones et al., 2005b). Conceptually, a person ofhigh integrity displays impulse restraint and conscience and has reduced hostility,aggression, and neurotic defenses. Individuals who are low on integrity have defi-cient superegos and display excessive aggressiveness and neurotic tendencies. Inorganizational settings, integrity functions to ensure work behavior according tosociety’s rules and norms.
The existing literature on integrity tests (cf. Ones et al., 1993) focuses primarilyon the prediction of typical performance (Sackett et al., 1988). Validities have beenreported for counterproductive behaviors, productivity (production records), andsupervisory ratings of job performance (see Ones & Viswesvaran, 1998, for a re-view). These criteria reflect typical performance. However, several potentialmechanisms can be postulated for the trait of integrity to influence maximal per-formance. Prevailing models of performance view all behavior, including maximalperformance, as a function of declarative knowledge, procedural knowledge, andmotivation (Campbell, 1990). The caveat here is that for maximal performance,motivational determinants are constrained (i.e., reduced in variability amongassessees). However, this does not necessarily translate into no motivational vari-ability. To the extent that integrity tests tap into motivational traits such as achieve-ment aspect of conscientiousness and emotional stability, they can be expected topredict maximal performance through motivational processes. The construct of in-tegrity is likely to be connected to putting forth effort under stressful maximal per-formance, suggesting the usefulness to integrity tests in predicting maximal per-formance. However, such a mechanism can be expected to be of more limited value
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for maximal performance than for the prediction of typical performance due to themotivational constraints in maximal performance.
Perhaps more centrally, the construct of integrity may be expected to predictmaximal performance through procedural knowledge. If individuals high on integ-rity behave according to societal and organizational expectations, they are likely toknow better what the expected behaviors are. In maximal performance situations,they may have a better sense of how to behave to perform the tasks at hand, to re-strain undesirable behaviors, to keep anxiety under control, and to potentially sup-port their performance by additional socially approved behaviors. Integrity islikely to influence how individuals go about performing tasks even in monitoredsettings and even when all individuals are attempting to perform at their peak per-formance. Given these conceptual reasons, it is important to examine the validityof integrity tests for measures of maximal performance. This was a unique ques-tion answered by our research. We expected integrity test scores to be related tomaximal performance.
Job knowledge and maximal performance. Job knowledge is one of thekey determinants of work performance (McCloy et al., 1994). Knowing what to do(declarative knowledge) and how to do it (procedural knowledge) are sharedcognitively based predictors of both maximal and typical performance. Job knowl-edge tests have been shown to have substantial criterion-related validity for bothtypical and maximal performance. The evidence for the validity of job knowledgemeasures predicting typical performance can be found in numerous meta-analysesreporting job knowledge validity for overall job performance (see Hunter &Hunter, 1984; Ones, Viswesvaran, & Dilchert, 2005a; Schmidt & Hunter, 1998).Similar large-scale evidence for job knowledge tests for predicting maximal per-formance comes from studies that report relationships between job knowledge andwork sample measures (e.g., Campbell & Knapp, 2001; Schmidt et al., 1986).Contemporary theories of performance support the notion that declarative and pro-cedural knowledge predict performing well on work samples, which in turn predicttypical performance (Borman, White, Pulakos, & Oppler, 1991; McCloy et al.,1994; Schmidt et al., 1986). Thus, we expected job knowledge to predict maximalperformance and included it in our study to provide a comparison point for our in-vestigation of the integrity–maximal performance link.
Joint contribution of integrity and job knowledge to the prediction of max-imal performance. The possibility of improving a selection system by design-ing an optimal combination of different predictors has always been an attractive re-search question for industrial–work–organizational psychologists. It is very rarethat personnel selection is based on a single predictor. To combine scores from dif-ferent predictors so as to maximize validity, minimize adverse impact and so on,predictor intercorrelations need to be estimated.
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In recent years, research has concentrated on intercorrelations between cogni-tive ability and other predictors. The primary reason for this is that cognitive abili-ties have been found to yield the highest validity across situations and settings(Schmidt, Ones, & Hunter, 1992). In this domain, prior studies have reported cor-relations between personality and cognitive ability (e.g., Day & Silverman, 1989).Based on previous meta-analyses, Schmidt and Hunter (1998) were able to esti-mate the correlation between cognitive ability tests and alternate predictors.Schmitt, Rogers, Chan, Sheppard, and Jennings (1997) and Bobko, Roth, andPotosky (1999) were also able to find intercorrelations between cognitive abilityand other predictors. However, relationships between other cognitive and non-cognitive predictors remain unreported. In fact, Bobko et al. (1999), Schmitt et al.(1997), and Cortina, Goldstein, Payne, Davison, and Gilliland (2000) were unableto find any correlations between popular noncognitive predictors in job-applicantsamples. In this study, another goal was to estimate the correlation between a jobknowledge test and an integrity test. We then used this information to estimate in-cremental validities for job knowledge and integrity measures each in the predic-tion of maximal performance.
In sum, previous research in personnel selection has compared cognitive andnoncognitive measures for predicting typical performance measures (e.g., supervi-sory ratings of job performance). However, direct comparisons of cognitive andpersonality-based measures for predicting maximal performance have been lack-ing. We offer first such comparisons using job knowledge as our focal cognitivelybased predictor and integrity as our focal noncognitive predictor. We assess the va-lidity of an integrity test and a job knowledge measure, both separately and in com-bination, for maximal performance. We also examine incremental validity of thesepredictors over each other for predicting maximal performance.
METHOD
Database and Measures
Data were collected from job applicants to skilled manufacturing jobs. The organi-zation was a Fortune 500 company specializing in the manufacture of many de-fense-related electrical and mechanical products. The data were collected whenone of the authors was serving as an external consultant to the organization. Theplant for which the hiring was intended was located in a midwestern state. Therewere 110 job applicants. All but 2 applicants were male.
The jobs for which the organization was hiring were mechanics, machine shopoperators, machine setters, machine builders, and machine shop supervisors. Mostjob duties for all jobs were similar in that they included setting up and adjustingmachine tools, fitting and assembling machine components according to blue-
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prints and sketches, repairing and maintaining machines and equipment, verifyingalignment and tolerances of parts, and applying knowledge of machine shop pro-cedures and techniques. The Dictionary of Occupational Titles codes for these jobswere 600–130–010, 600–281–022, and 600–360–014.
All applicants completed a personality-based integrity test—the Personnel Re-action Blank (Gough, 1954). The Personnel Reaction Blank is a well-respectedpersonality-based integrity test (Ones et al., 1993), with substantial data support-ing its construct and criterion-related validity (Viswesvaran & Ones, 1997). Part 1of the instruments asks about occupational preferences and Part 2 about views andexperiences. An overall score of integrity is calculated based on scored items fromboth parts. It was initially created in 1950s based on what was then called the De-linquency scale of the California Psychological Inventory, which has since devel-oped into the Socialization scale. The more recently revised version of the Person-nel Reaction Blank (PRB; Gough, Arvey, & Bradley, 2004) was used in thisresearch. Detailed reliability, factor structure, and validity information on the PRBis provided in the technical manuals for the test (Gough, 1971; Gough et al., 2004)and published reviews (Hough, 1990; Viswesvaran & Ones, 1997).
Following the completion of the integrity test, the applicants took a job knowl-edge test. The job knowledge measure was a custom-made multiple-choice instru-ment composed of 40 items, scored right or wrong. It was constructed and vali-dated for the organization by a consultant industrial–organizational psychologistin the late 1970s. The test measured knowledge of mechanical principles and me-chanical movements as well as knowledge of reading prints and drawings. The jobknowledge test scores could range from 1 to 200 and had a KR–20 reliability of .90in this sample.
The maximal performance criterion was assessed using a hands-on work sam-ple measure. Job applicants were asked to perform the job they were a candidatefor at three different workstations. Applicants performed the work sample to dem-onstrate that they could actually do the job. Tasks included setting up and adjustingmachine tools, fitting and assembling machine components according to blue-prints, verifying alignment and tolerances of parts, and applying knowledge of ma-chine shop procedures. The work sample took about 3 hours to complete. As such,this measure captured the can do aspects of job performance. The scores on thework sample measure were assigned by supervisors who observed applicants per-forming and could range from 1 to 100. Each rater assigned an overall score towork sample performance separately at each of the three workstations. Two ratersobserved each applicant. The interrater reliability of scores on the composite of thethree work sample measures (in this particular sample) was .74.
Unfortunately, not all applicants completed the job knowledge and the worksample measures. Eighty-six of the 110 applicants took the job knowledge tests,and 67 of the 86 completed the maximal performance measures. The reduction insample size was based on, primarily, applicants choosing not to continue with the
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selection process upon realizing that they would have to progressively take jobknowledge tests and that they would then have to actually perform work under ob-servation. Note also that there was no selection done by the organization on the ba-sis of integrity scores prior to the applicants being tested for job knowledge orwork sample performance. Therefore, we do not make any direct range restrictioncorrections when reporting correlations.
RESULTS
Observed and corrected intercorrelations among the measures are reported in Ta-ble 1. Job knowledge tests had an observed validity of .36 for predicting maximalperformance. Compared to job knowledge tests, integrity test scores had a some-what lower validity of .27. The criterion unreliability corrected operational validi-ties were .42 and .31 for the job knowledge and integrity measures, respectively.The job knowledge measure and the integrity test correlated .14.
Regression analyses using both integrity and job knowledge resulted in a multi-ple R of .49. The operational variance accounted for by job knowledge over the in-tegrity measure for predicting maximal performance was .14. Conversely, opera-tional variance accounted for by the integrity test over the job knowledge measurewas .07.
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TABLE 1Intercorrelations Among Variables
Predictors (1, 2)/Criterion (3)
Correlations Regression
1 2 3 β R / R2 ∆R2
1 Integrity (PRB) .80 .31 .26 .07(.13 to .50)
2 Job knowledge .14 .90 .42 .38 .14(–.03 to .31) (.25 to .59)
3 Maximal performance .27 .36 .74 .49 / .24(.08 to .46) (.18 to .54)
Note. Observed correlations are presented below the diagonal; operational validities (correctedfor unreliability in the criterion measure only) are in bold and presented above the diagonal. Values onthe diagonal represent reliability estimates. Confidence intervals (90%, two-tailed) are presented in pa-rentheses. Total sample sizes (Ns) were 110 for the integrity test and 86 for the job knowledge test.Pairwise N with the criterion measure was 67. The regression is based on operational validities and theobserved predictor intercorrelation. Regression results are for including both integrity and job knowl-edge measures in the prediction of maximal performance. ∆R2 indicates incremental variance ac-counted for by adding the predictor represented in the row over the other predictor. PRB = PersonnelReaction Blank.
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DISCUSSION
There are several unique contributions of this study. First, this was the first investi-gation of the validity of integrity tests for predicting maximal performance. Sec-ond, it provided a direct comparison of a cognitively based (job knowledge) and apersonality-based (integrity) measure for predicting maximal performance. Bothmeasures were predictive of the maximal performance criterion and their confi-dence intervals overlapped. Furthermore, of interest, the confidence intervals ofthe validities for both predictor measures in the prediction of maximal perfor-mance included their meta-analytic point estimates reported in previous researchfor typical performance (Schmidt & Hunter, 1998). Third, we assessed the rela-tionship between two predictors of maximal performance and examined the incre-mental value of these predictors over each other.
Of course, the sample employed in this study came from one organization andwas comprised of medium-complexity skilled jobs. Future research is needed withlarger samples, different jobs, and different organizations to test thegeneralizability of the findings. Given the importance of assessing intercorrela-tions among predictors in job-applicant samples, we hope this study generatesmore research along these lines. Results reported here should facilitate futuremeta-analytic efforts to estimate the intercorrelations among predictors in appli-cant samples. The results reported here should also be useful for practitioners indesigning selection systems as well as being useful to researchers in facilitating thedevelopment of comprehensive theories of work behavior by providing estimatesof how different predictors correlate in applicant samples and how they relate tomeasures of maximal performance.
In this research note, we examined the independent and combined effects of in-tegrity and job knowledge on maximum performance. Previous papers on typicaland maximal performance have suggested that cognitively based predictors shouldpredict maximal performance and noncognitive measures should have limited use-fulness for the same criterion. By offering evidence that integrity tests can predictmaximal performance, we provide a justification for future studies to further ex-amine the mechanisms through which the construct of integrity influences maxi-mal performance (i.e., procedural knowledge and motivation). Such future re-search can also establish the relative importance of motivational processes versusprocedural knowledge in both typical and maximal performance. Another avenuefor future research may entail partitioning the explanatory variance in predictorsinto maximal and typical components. Such investigations of interactive effectsare likely to enrich our understanding of both the predictor (e.g., Digman’s factoralpha) and criterion constructs. Finally, future research is needed to assess theintercorrelations in other applicant samples so as to facilitate constructions of theselection models and integrated selection systems.
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ACKNOWLEDGMENT
Both authors contributed equally; the order of authorship is arbitrary.
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