Predictive validity of selection tools: The critical role of applicant-pool composition.

What is it about?

Just how well do selection tools—such as admission tests, interviews, or job assessments—actually predict the future performance of applicants? This study shows that the tools’ predictive accuracy depends not only on the quality of the tool itself but also on who applies. Drawing on a formal statistical analysis and a large-scale dataset, we demonstrate empirically that variations in applicant-pool composition can substantially alter the predictive accuracy of even well-designed selection measures. This insight applies broadly to both educational and personnel selection, highlighting that choosing the optimal selection tool depends critically on understanding the composition of the pool from which candidates are drawn. The findings of this research can help practitioners and decision-makers anticipate changes in predictive accuracy and, if needed, adapt their selection process, enabling them to identify those selection tools that are positively impacted by anticipated changes in applicant-pool composition.

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Photo by Nguyen Dang Hoang Nhu on Unsplash

Photo by Nguyen Dang Hoang Nhu on Unsplash