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Do selection tests “really” work better than we think they do?

Published online by Cambridge University Press:  27 August 2024

S. Burak Ozkum Open the ORCID record for S. Burak Ozkum [Opens in a new window]
S. Burak Ozkum*
Affiliation:
Department of Psychology, Illinois State University, Normal, IL, USA
*
Email: sbozkum@ilstu.edu
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Abstract

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Type
Commentaries
Information
Industrial and Organizational Psychology , First View , pp. 1 - 5
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Society for Industrial and Organizational Psychology

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References

Borman, W. C., & Brush, D. H. (1993). More progress toward a taxonomy of managerial performance requirements. Human Performance, 6(1), 1–21. https://doi.org/10.1207/s15327043hup0601_1 CrossRefGoogle Scholar
Campbell, J. P. (1990). Modeling the performance prediction problem in industrial and organizational psychology. In Dunnette, M. D., & Hough, L. M. (Ed.), Handbook of industrial and organizational psychology (2nd ed. pp. 687–732). Consulting Psychologists Press.Google Scholar
Foster, J., Steel, P., Harms, P., O’Neill, T. A., & Wood, D. (2024). Selection tests work better than we think they do, and have for years. Industrial and Organizational Psychology, 17.Google Scholar
IBM Corp 2023). IBM SPSS Statistics for Windows. IBM Corp.Google Scholar
Jackson, D. J. R., Michaelides, G., Dewberry, C., Schwencke, B., & Toms, S. (2020). The implications of unconfounding multisource performance ratings. Journal of Applied Psychology, 105(3), 312–329. https://doi.org/10.1037/apl0000434 CrossRefGoogle ScholarPubMed
Jones, D. N., & Figueredo, A. J. (2013). The core of darkness: Uncovering the heart of the dark triad. European Journal of Personality, 27(6), 521–531.CrossRefGoogle Scholar
Nimon, K. F., & Oswald, F. L. (2013). Understanding the results of multiple linear regression: Beyond standardized regression coefficients. Organizational Research Methods, 16(4), 650–674. https://doi.org/10.1177/1094428113493929 CrossRefGoogle Scholar
O’Neill, T. A., McLarnon, M. J. W., & Carswell, J. J. (2015). Variance components of job performance ratings. Human Performance, 28(1), 66–91. https://doi.org/10.1080/08959285.2014.974756 CrossRefGoogle Scholar
Sackett, P. R., Zhang, C., Berry, C. M., & Lievens, F. (2022). Revisiting meta-analytic estimates of validity in personnel selection: Addressing systematic overcorrection for restriction of range. Journal of Applied Psychology, 107(11), 2040–2068. https://doi.org/10.1037/apl0000994 CrossRefGoogle ScholarPubMed
Schlaegel, C., Engle, R. L., & Lang, G. (2022). The unique and common effects of emotional intelligence dimensions on job satisfaction and facets of job performance: An exploratory study in three countries. International Journal of Human Resource Management, 33(8), 1562–1605. https://doi.org/10.1080/09585192.2020.1811368 CrossRefGoogle Scholar
Schoen, J. L., DeSimone, J. A., & James, L. R. (2011). Exploring joint variance between independent variables and a criterion: Meaning, effect, and size. Organizational Research Methods, 14(4), 674–695. https://doi.org/10.1177/1094428110381787 CrossRefGoogle Scholar
Shuck, B., Nimon, K., & Zigarmi, D. (2017). Untangling the predictive nomological validity of employee engagement: Partitioning variance in employee engagement using job attitude measures. Group & Organization Management, 42(1), 79–112. https://doi.org/10.1177/1059601116642364 CrossRefGoogle Scholar
Tett, R. P., Jackson, D. N., Rothstein, M., & Reddon, J. R. (1999). Meta-analysis of bidirectional relations in personality-job performance research. Human Performance, 12(1), 1–29. https://doi.org/10.1207/s15327043hup1201_1 CrossRefGoogle Scholar
Viswesvaran, C., & Ones, D. S. (2000). Perspectives on models of job performance. International Journal of Selection and Assessment, 8(4), 216–226. https://doi.org/10.1111/1468-2389.00151 CrossRefGoogle Scholar
Viswesvaran, C., Schmidt, F. L., & Ones, D. S. (2005). Is there a general factor in ratings of job performance? A meta-analytic framework for disentangling substantive and error influences. Journal of Applied Psychology, 90(1), 108–131. https://doi.org/10.1037/0021-9010.90.1.108 CrossRefGoogle Scholar
Vize, C. E., Collison, K. L., Miller, J. D., & Lynam, D. R. (2020). The “core” of the dark triad: A test of competing hypotheses. Personality Disorders: Theory, Research, and Treatment, 11(2), 91–99. https://doi.org/10.1037/per0000386 CrossRefGoogle Scholar