Combining Predictors to Achieve Optimal Trade-Offs Between Selection Quality and Adverse Impact

The authors propose a procedure to determine (a) predictor composites that result in a Pareto-optimal trade-off between the often competing goals in personnel selection of quality and adverse impact and (b) the relative importance of the quality and impact objectives that correspond to each of these trade-offs. They also investigated whether the obtained Pareto-optimal composites continue to perform well under variability of the selection parameters that characterize the intended selection decision. The results of this investigation indicate that this is indeed the case. The authors suggest that the procedure be used as one of a number of potential strategies for addressing the quality-adverse impact problem in settings where estimates of the selection parameters (e.g., validity estimates, predictor intercorrelations, subgroup mean differences on the predictors and criteria) are available from either a local validation study or meta-analytic research.