Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-12-01T01:37:18.446Z Has data issue: false hasContentIssue false
  • English
  • Français

Conceptual and Technical Issues in Conducting and Interpreting Differential Prediction Analyses

Published online by Cambridge University Press:  07 January 2015

Paul R. Sackett  and
Philip Bobko
Paul R. Sackett*
Affiliation:
University of Minnesota
Philip Bobko
Affiliation:
Gettysburg College
*
E-mail: [email protected], Address: Department of Psychology, University of Minnesota, Elliott Hall, 75 East River Road, Minneapolis, MN 55455
Get access Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 3 , Issue 2 , June 2010 , pp. 213 - 217
Copyright
Copyright © Society for Industrial and Organizational Psychology 2010 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bobko, P. (2001). Correlation and regression: Principles and applications for industrial/organizational psychology and management (2nd ed.). Thousand Oaks, CA: Sage.Google Scholar
Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
Gocka, E. (1974). Coding for correlation and regression. Educational and Psychological Measurement, 34, 771–783.10.1177/001316447403400404Google Scholar
Hunter, J., & Schmidt, F. (2000). Racial and gender bias in ability and achievement tests: Resolving the apparent paradox. Psychology, Public Policy, and Law, 6, 151–158.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, 738–761.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, 192–205.Google Scholar
Sackett, P. R., Borneman, M. J., & Connelly, B. S. (2008). High-stakes testing in higher education and employment: Appraising the evidence for validity and fairness. American Psychologist, 63, 215–227.10.1037/0003-066X.63.4.215Google Scholar
Sackett, P. R., Laczo, R. M., and Lippe, Z. P. (2003). Differential prediction and the use of multiple predictors: The omitted variables problem. Journal of Applied Psychology, 88, 1046–1056.Google Scholar
Society for Industrial and Organizational Psychology (SIOP). (2003). Principles for the validation and use of personnel selection procedures (4th ed.). Bowling Green, OH: Society for Industrial and Organizational Psychology.Google Scholar
U.S. Equal Employment Opportunity Commission, U.S. Civil Service Commission, U.S. Department of Labor, U.S. Department of Justice (1978). Uniform guidelines on employee selection procedures. Federal Register, 43, 38295–38309.Google Scholar