Validity and adverse impact potential of predictor composite formation

Previous research on the validity and adverse impact (AI) of predictor composite formation focused on the merits of regression-based or ad hoc composites. We argue for a broader focus. Ad hoc chosen composites are usually not Pareto-optimal, whereas the regression-based composite represents only one element from the total set of Pareto-optimal composites and can, therefore, provide only limited information on the potential for validity and AI reduction of forming predictor composites when both validity and AI are of concern. In that case, other Pareto-optimal composites may provide a better benchmark to decide on the merits of the predictor composite formation. We summarize a method to determine the set of Pareto-optimal composites and apply the method to a representative collection of selection predictors. The application shows that the assessment of the AI and validity of predictor composite formation can differ substantially from the one arrived at when considering only regression-based composites.