Multi-subgroup Pareto-optimal weighting

There are methods in personnel selection that seeks to improve diversity in the hiring outcomes. Among them, one of the most promising methods is Pareto-optimal weighting via the normal boundary intersection algorithm (De Corte et al.

Orthogonalized Criterion Weighting

In this project, we introduce another Pareto-optimal weighting method that incorporates a priori inputs regarding relative importance of criterion validity (i.e., expected job performance) and diversity in hiring outcomes. This project is in collaboration with Daniel A.

Item-level faking detection

Scholars have argued that faking happens on the item-level (KKuncel & Tellegen, 2001) and has complex patterns (e.g., non-linearity; Kuncel & Borneman, 2007). In this project, we put this argument directly to test.