Weighing job performance predictors to both maximize the quality of the selected workforce and control the level of adverse impact.

Abstract

Considerable thought has been given to the effects of various strategies of weighing predictor information on adverse impact, minority hiring, and quality of the selected workforce. However, these efforts do not solve the dilemma faced by employers who want to achieve an optimally qualified workforce but at the same time want to eliminate adverse impact. To remove this limitation, the present article shows how constrained nonlinear programming can be used to combine job performance predictors into a predictor composite such that the average quality of the composite selected employees is maximized, the intended overall selection rate is achieved, and the adverse impact ratio remains within acceptable bounds. Although the new procedure allows for situations in which the performance criterion is multidimensional, a further extension is needed to handle multistage selection decisions.