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WORK SAMPLE TESTS IN PERSONNEL SELECTION: A META‐ANALYSIS OF BLACK–WHITE DIFFERENCES IN OVERALL AND EXERCISE SCORES
Author(s) -
ROTH PHILIP,
BOBKO PHILIP,
McFARLAND LYNN,
BUSTER MAURY
Publication year - 2008
Publication title -
personnel psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.076
H-Index - 142
eISSN - 1744-6570
pISSN - 0031-5826
DOI - 10.1111/j.1744-6570.2008.00125.x
Subject(s) - psychology , personnel selection , sample (material) , ethnic group , social psychology , test (biology) , meta analysis , white (mutation) , applied psychology , statistics , medicine , paleontology , biochemistry , chemistry , mathematics , chromatography , sociology , anthropology , gene , biology
Work sample exams are generally thought to have either low or comparatively low levels of ethnic group differences when used for personnel selection. Such exams are sometimes called “simulation exercises” and involve having applicants perform a set of tasks that are similar to those performed on the job. The nearly ubiquitous meta‐analytic value of Black–White subgroup differences in the literature is d = .38. Unfortunately, this estimate is plagued by a variety of problems (e.g., range restriction, inclusion of nonwork sample tests). Further, there are virtually no analyses that examine how the saturation of different constructs influence work sample tests. We gathered available data for Black–White ethnic group differences and found that overall work sample differences were markedly larger for samples of job applicants ( d = .73) than previously thought. We also examined how different exercises and saturation of different constructs influenced work sample d s. For example, work sample test ratings of cognitive and job knowledge skills were associated with a mean observed d = .80, whereas ratings of various social skills were associated with mean observed d s that varied from .21 to .27. We urge scientists and practitioners to consider both the method and the constructs that are targeted when forecasting predictor d s.