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Final Thoughts on Measurement Bias and Differential Prediction

Published online by Cambridge University Press:  07 January 2015

Adam W. Meade*
Affiliation:
North Carolina State University
Scott Tonidandel
Affiliation:
Davidson College
*
E-mail: awmeade@ncsu.edu, Address: Department of Psychology, North Carolina State University, Campus Box 7650, Raleigh, NC 27695-7650

Abstract

In the focal article, we suggested that more thought be given to the concepts of test bias, measurement bias, and differential prediction and the implicit framework of fairness underlying the Cleary model. In this response, we clarify the nature and scope of our recommendations and address some of the more critical comments of our work.

Type
Response
Copyright
Copyright © Society for Industrial and Organizational Psychology 2010 

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Footnotes

*

Department of Psychology, North Carolina State University

**

Department of Psychology, Davidson College.

References

Borneman, M. J. (2010). Using meta-analysis to increase power in differential prediction analyses. Industrial and Organizational Psychology, 3, 224227.Google Scholar
Camilli, G., & Shepard, L. A. (1994). Methods for identifying biased test items. Thousand Oaks, CA: Sage.Google Scholar
Chi, M. T. H., Siler, S. A., & Jeong, H. (2004). Can tutors monitor students' understanding accurately? Cognition and Instruction, 22, 363387.Google Scholar
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: L. Erlbaum.Google Scholar
Colarelli, S. M., Han, K., & Yang, C. (2010). Biased against whom? The problems of “group” definition and membership in test bias analyses. Industrial and Organizational Psychology, 3, 228231.Google Scholar
Cronshaw, S. F., & Chung-Yan, G. (2010). The need for even further clarity about Cleary. Industrial and Organizational Psychology, 3, 206209.Google Scholar
Jaccard, J., & Turrisi, R. (2003). Interaction effects in multiple regression (2nd ed.). Thousand Oaks, CA: Sage.Google Scholar
Meade, A. W., & Fetzer, M. (2009). Test bias, differential prediction, and a revised approach for determining the suitability of a predictor in a selection context. Organizational Research Methods, 12, 738761.Google Scholar
Meade, A. W., & Tonidandel, S. (2010). Not seeing clearly with Cleary: What test bias analyses do and do not tell us. Industrial and Organizational Psychology, 3, 192205.Google Scholar
Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction (3rd ed.). Fort Worth, TX: Harcourt Brace College.Google Scholar
Putka, D. J., Trippe, D. M., & Vasilopoulos, N. L. (2010). Diagnosing when evidence of bias is problematic: Methodological cookbooks and the unfortunate complexities of reality. Industrial and Organizational Psychology, 3, 218223.Google Scholar
Sackett, P. R., & Bobko, P. (2010). Conceptual and technical issues in conducting and interpreting differential prediction analyses. Industrial and Organizational Psychology, 3, 213217.Google Scholar
Sackett, P. R., Laczo, R. M., & Lippe, Z. P. (2003). Differential prediction and the use of multiple predictors: The omitted variables problem. Journal of Applied Psychology, 88, 10461056.Google Scholar
Woehr, D. J. (2010). What test bias analyses do and don't tell us: Let's not assume we have a can opener. Industrial and Organizational Psychology, 3, 210212.Google Scholar