Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-27T15:38:54.270Z Has data issue: false hasContentIssue false
  • English
  • Français

Diagnosing When Evidence of Bias Is Problematic: Methodological Cookbooks and the Unfortunate Complexities of Reality

Published online by Cambridge University Press:  07 January 2015

Dan J. Putka ,
D. Matthew Trippe  and
Nicholas L. Vasilopoulos
Dan J. Putka*
Affiliation:
Human Resources Research Organization
D. Matthew Trippe
Affiliation:
Human Resources Research Organization
Nicholas L. Vasilopoulos
Affiliation:
Human Resources Research Organization
*
E-mail: [email protected], Address: Human Resources Research Organization (HumRRO), 66 Canal Center Plaza, Suite 700, Alexandria, VA 22314
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. 218 - 223
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.)

Footnotes

*

Human Resources Research Organization (HumRRO).

References

Berry, W. D. (1993). Understanding regression assumptions. Newbury Park, CA: Sage.Google Scholar
Bobko, P. (2001). Correlation and regression: Principles and applications for industrial-organizational psychology and management (2nd ed.). Thousand Oaks, CA: Sage.10.4135/9781412983815Google Scholar
Camilli, G., & Shepard, L. A. (1994). Methods for identifying biased test items. Newbury Park, CA: Sage.Google Scholar
Candell, G. L., & Drasgow, F. (1988). An iterative procedure for linking metrics and assessing item bias in item response theory. Applied Psychological Measurement, 12, 253–260.Google Scholar
Cleary, T. A. (1968). Test bias: Prediction of grades of Negro and White students in integrated colleges. Journal of Educational Measurement, 5, 115–124.10.1111/j.1745-3984.1968.tb00613.xGoogle Scholar
Fox, J. (1991). Regression diagnostics. Newbury Park, CA: Sage.10.4135/9781412985604Google Scholar
Holland, P. W., & Thayer, D. T. (1988). Differential item performance and the Mantel-Haenzel procedure. In Wainer, H., & Braun, H. I. (Eds.), Test validity (pp. 129–145). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
Lautenshlager, G. J., & Mendoza, J. L. (1986). A step-down hierarchal multiple regression analysis for examining hypotheses about test bias in prediction. Applied Psychological Measurement, 10, 133–139.10.1177/014662168601000202Google Scholar
McKay, P. F., & McDaniel, M. A. (2006). A reexamination of Black-White mean differences in work performance: More data, more moderators. Journal of Applied Psychology, 91, 538–554.Google Scholar
McNemar, Q. (1975). On so-called test bias. American Psychologist, 30, 848–851.10.1037/h0077026Google 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, 738–761.10.1177/1094428109331487Google 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, 192–205.10.1111/j.1754-9434.2010.01223.xGoogle Scholar
Murphy, K. R. (2008). Explaining the weak relationship between job performance and ratings of job performance. Industrial and Organizational Psychology, 1, 148–160.10.1111/j.1754-9434.2008.00030.xGoogle Scholar
Neter, J., Kutner, M. H., Nachtsheim, C. J., & Wasserman, W. (1996). Applied linear statistical models (4th ed.). New York: McGraw-Hill.Google Scholar
Raju, N. S., Van der Linden, W., & Fleer, P. (1995). An IRT-based internal measure of test bias with applications for differential item functioning. Applied Psychological Measurement, 19, 353–368.10.1177/014662169501900405Google Scholar
Serlin, R. C., & Levin, J. R. (1980). Identifying regions of significance in aptitude-by-treatment-interaction research. American Educational Research Journal, 17, 389–399.Google Scholar
Stark, S., Chernyshenko, O. S., & Drasgow, F. (2004). Examining the effects of differential item functioning and differential test functioning on selection decisions: When are statistically significant effects practically important? Journal of Applied Psychology, 89, 497–508.10.1037/0021-9010.89.3.497Google Scholar