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De Corte, Lievens, and Sackett add to the literature on selection test validity and adverse impact (AI). Their Pareto-based weighting scheme essentially asks organizations if they are willing to give up some validity to hopefully achieve some reduction in AI. We considered their approach and conclusions in relation to the regression weighting method we used, and we offer five points that reflect our observations as well as our shared goals. We hope our comments, like their work in this field, will invigorate the pursuit of new ways of examining, and one day resolving, the persistent concern regarding the AI associated with valid selection tests. [ABSTRACT FROM AUTHOR]

Although there has been empirical attention paid to the criterion-related validity of predictor composites, there has been much less attention paid to the standardized ethnic group differences associated with these composites. One important area of inquiry in predictor composite research is the influence of adding predictors to a test of general mental ability. The limited empirical literature on this practice is mixed, but the prevailing expectation is that there is likely to be higher validity and less adverse impact. Unfortunately, much of the previous work is limited by the presence of inaccurate validity and standardized ethnic group difference values. In this analysis we formed meta-analytic matrices to more accurately estimate the validity and standardized ethnic group differences of several composites that combine a measure of cognitive ability with measures of conscientiousness, a structured interview, or biodata. While results were somewhat complex, we found that adding alternative predictors does not result in a situation in which validity automatically goes up and adverse impact potential automatically goes down. In fact, the reductions in adverse impact (if any) from adding “non-cognitive” predictors were more modest than much of the literature suggests. [ABSTRACT FROM AUTHOR]

A variety of recent articles in the personnel selection literature have used analyses of meta-analytically derived matrices to draw general conclusions for the field. The purpose of this article is to construct a matrix that incorporates as complete information as possible on the relationships among cognitive ability measures, three sets of alternative predictors, and job performance. We build upon a starting matrix used by Schmitt, Rodgers, Chan, Sheppard, and Jennings (1997). Mean differences, by race, for each of the measures and the potential for adverse impact of predictor composites are also considered. We demonstrate that the use of alternative predictors alone to predict job performance (in the absence of cognitive ability) lowers the potential for adverse impact. However, in contrast to recent claims, adverse impact continues to occur at many commonly used selection ratios. Future researchers are encouraged to use our matrix and to expand upon it as new primary research becomes available. We also report and reaffirm many methodological lessons along the way, including the many judgment calls that appear in an effort of this magnitude and a reminder that the field could benefit from even greater conceptual care regarding what is labeled an "alternative predictor." Directions for future meta-analyses and for future primary research activities are also derived. [ABSTRACT FROM AUTHOR]