Typically, when practitioners are designing a selection system, they are looking for ways to maximize the quality and diversity of the individuals hired. Meeting this goal can be challenging, and in confronting the dilemmas associated with this goal, practitioners looking to design the ideal selection system have a number of decisions to make. For instance, they must decide which predictors to use, whether low scores on one predictor can be balanced out by high scores on another predictor (that is, will the selection system be compensatory), and the sequence of administering the multiple predictors/screens. There are many considerations when making these decisions, including level of resources (e.g. time, money), and the characteristics of both the job and the applicant pool.
WHAT IS A PARETO-OPTIMAL SYSTEM?
One way in which these considerations can be effectively accounted for is through the use of a Pareto-optimal selection system. This means the new solution is identified when no other solution is at least as good on all outcome criteria (predictive validity, impact to diversity) and has at least one outcome that is more favorable. De Corte and colleagues discuss a method by which selection systems can be electronically-generated and evaluated for use in a particular situation, in order to identify a system that most effectively balances the tradeoffs inherent in different systems (that is, a Pareto-optimal system).
THE BOTTOM LINE
Ideally, research on Pareto-optimal selection systems will continue, as their use can minimize adverse impact without sacrificing the quality of hires. In the meantime, practitioners should consider using the method that De Corte et al. suggest when designing a selection system. Doing so should result in the design of a selection system that maximizes oft-divergent, dual goals: a system that is both fair and effective.
De Corte, W., Sackett, P. R., & Lievens, F. (2011). Designing Pareto-optimal selection systems: Formalizing the decisions required for selection system development. Journal of Applied Psychology, 96, 907-926.
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