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Selection tests work better than we think they do, and have for years

Published online by Cambridge University Press:  20 August 2024

Jeff L. Foster Open the ORCID record for Jeff L. Foster [Opens in a new window] ,
Piers Steel ,
Peter D. Harms ,
Thomas A. O’Neill  and
Dustin Wood
Jeff L. Foster*
Affiliation:
Missouri State University, Springfield, MO, USA
Piers Steel
Affiliation:
University of Calgary, Calgary, AB, Canada
Peter D. Harms
Affiliation:
University of Alabama, Tuscaloosa, AL, USA
Thomas A. O’Neill
Affiliation:
University of Calgary, Calgary, AB, Canada
Dustin Wood
Affiliation:
University of Alabama, Tuscaloosa, AL, USA
*
Corresponding author: Jeff L. Foster; Email: jfoster@missouristate.edu
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Abstract

We can make better decisions when we have a better understanding of the different sources of variance that impact job performance ratings. A failure to do so cannot only lead to inaccurate conclusions when interpreting job performance ratings, but often misguided efforts aimed at improving our ability to explain and predict them. In this paper, we outline six recommendations relating to the interpretation of predictive validity coefficients and efforts aimed at predicting job performance ratings. The first three focus on the need to evaluate the effectiveness of selection instruments and systems based only on the variance they can possibly account for. When doing so, we find that it is not only possible to account for the majority of the variance in job performance ratings that most select systems can possibly predict, but that we’ve been able to account for this variance for years. Our last three recommendations focus on the need to incorporate components related to additional sources of variance in our predictive models. We conclude with a discussion of their implications for both research and practice.

Keywords

job performance ratingsjob performanceemployee selectioncriterion-related validity

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Type
Focal Article
Information
Industrial and Organizational Psychology , Volume 17 , Issue 3 , September 2024 , pp. 269 - 282
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Society for Industrial and Organizational Psychology

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