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Predictive Bias in Work and Educational Settings
Abstract
In this chapter we review the research and concept of predictive bias, present a new theory of when and how predictive bias will occur, and create a list of methodological and substantive confounds that complicate research on predictive bias. We note that educational and organizational researchers have often used different methods to operationalize the same concept in academic and work settings, respectively. We review these approaches and recommend a modified method based on the examination of regression plots and residuals. A new theory is presented that addresses how and when predictive bias would occur using the existing definition (i.e., the Cleary Model). Theoretically, we suggest that a performance determinants framework provides a good foundation for understanding how and when predictive bias can occur. We illustrate key concepts in our theory using existing research. We suggest that the nature of predictive bias is dependent on the reason a predictor is correlated with subsequent performance. We then use this theory to guide a review of previous research testing for the presence of predictive bias across a range of personnel selection and academic admissions tools. Although much of the literature indicates that professional selection tools are unbiased for native speakers of English in the U.S., the most troubling finding is the scarcity of information for many of the most popular selection and admissions tools.
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“However, intercept differences do not unequivocally demonstrate test bias.Instead, they may arise from a range of other explanations, including omitted variables bias (OVB), measurement artifacts, or deficiencies in how the predictor or criterion is defined (Kuncel & Klieger, 2012).”
From An Omitted Variables Approach to Disentangling Differential Prediction in College Admissions (2026) · cites this paper as Kuncel & Klieger, 2012
In the citing paper
“While this reduces variability in both predictors and criteria, this mirrors the context in which admissions decisions are made, making uncorrected estimates more relevant (AERA et al., 2014;Kuncel & Klieger, 2012).”
From An Omitted Variables Approach to Disentangling Differential Prediction in College Admissions (2026) · cites this paper as Kuncel & Klieger, 2012
Citations
Cites
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Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2001) · Psychological Bulletin · doi:10.1037/0033-2909.127.1.162 · Cited by 639
Self-citationPersonality measurement and employment decisions: Questions and answers.
Hogan, R., Hogan, J., & Roberts, B. W. (1996) · American Psychologist · doi:10.1037/0003-066x.51.5.469 · Cited by 572
Sackett, P. R., Schmitt, N., Ellingson, J. E., & Kabin, M. B. (2001) · American Psychologist · doi:10.1037/0003-066x.56.4.302 · Cited by 387
CONSIDERATIONS FOR STUDIES OF TEST BIAS
LINN, R. L., & WERTS, C. E. (1971) · Journal of Educational Measurement · doi:10.1111/j.1745-3984.1971.tb00898.x · Cited by 113
Differential Prediction and the Use of Multiple Predictors: The Omitted Variables Problem.
Sackett, P. R., Laczo, R. M., & Lippe, Z. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.6.1046 · Cited by 62
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Cited by
Sorted by citations
An Omitted Variables Approach to Disentangling Differential Prediction in College Admissions
Demeke, S., Sackett, P. R., Kuncel, N. R., & Lee, S. D. (2026) · Educational Measurement: Issues and Practice · doi:10.1111/emip.70048
Self-citation
Metadata
- Authors
- Kuncel, N. R., & Klieger, D. M.
- Affiliations
- University of Minnesota (US)
- Educational Testing Service (US)
- Venue
- The Oxford Handbook of Personnel Assessment and Selection
- Publisher
- Oxford University Press
- Published
- 2012-11-21
- Pages
- 462-484
- OpenAlex
- W1657112078
- Cited by
- 4
- References
- 6
- Citations by year
- 2017: 1 · 2016: 1 · 2014: 1 · 2013: 1
- FWCI
- 1.87
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Oxford University Press eBooks (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Education › Medical Education and Admissions
- Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
- Keywords
- predictive bias, personnel selection, university admission, performance factors, selection tools, native English speakers, information scarcity
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