On the Quantification and Generalizability of Differential Prediction in Selection Systems
2019-05
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On the Quantification and Generalizability of Differential Prediction in Selection Systems
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2019-05
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Differential prediction analyses are important for personnel psychologists to determine whether the regression lines linking a predictor variable to a criterion/performance variable are comparable between a referent group and a legally protected focal group. Although many decades of research on cognitive tests has indicated that differential prediction does occur for racial/ethnic minority groups in the U.S. relative to Whites, the bulk of evidence has indicated that these differences result into the overprediction of Black and Hispanic individuals’ performance from cognitive test scores, which does not indicate predictive bias against these groups. However, research published over the past decade by Aguinis Culpepper, and Pierce (2010; 2016) has questioned the accuracy and generalizability of past findings, arguing that the historic trends could have been caused by statistical artifacts. In a series of four studies, I present methodological advancements in the quantification of differential prediction and supply substantive analyses that refute the findings reported by Aguinis et al. (2010; 2016). Specifically, I (1) offer derivations of simplified effect-size estimation procedures for differential prediction analyses with accompanying standard-error estimators, (2) illustrate the effects of composite predictors on differential prediction effects, (3) demonstrate the generalizability of White-minority and male-female differential prediction in the post-secondary education admissions domain, and (4) present findings from a simulation study designed to identify which features of selection systems could cause statistical artifacts to bias the results of differential prediction analyses conducted on cognitive test scores.
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University of Minnesota Ph.D. dissertation. May 2019. Major: Psychology. Advisors: Paul Sackett, Nathan Kuncel. 1 computer file (PDF); xix, 321 pages.
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Dahlke, Jeffrey. (2019). On the Quantification and Generalizability of Differential Prediction in Selection Systems. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/206301.
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