For decades, it has been suggested organizations utilize mechanical decisions. Unfortunately, organizations continue to rely on holistic judgments. Perhaps part of the reasons organizations continue to rely on judgment when mak- ing decisions is because the reported statistics associated with mechanical judgments (e.g., R2) are not intuitive to stakeholders. For example, it is unclear in terms of turnover whether an R2 change from 0.23 under the old (holistic) system to an R2 of 0.27 under the (mechanical) system is enough of an improvement to justify the cost of changing selection methods. In this paper, we argue that researchers instead report changes in criteria of interest (e.g., absenteeism rate before versus after utilizing a mechanical selection sys- tem). This can be accomplished by utilizing a multiple imputation algorithm that simulates optimal selection decisions. Additionally, we provide both an R-package, as well as a point-and-click Shiny app that allows researchers to easily estimate intuitive statistics (e.g., improvement in turnover, proportion of applicants for which an optimal and holistic system agree).
Intuitive statistics by 8-month-old infants
