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Best Practice Recommendations for Conducting Key Driver Analyses

Published online by Cambridge University Press:  29 June 2017

Jeff W. Johnson*
Affiliation:
CEB
*
Correspondence concerning this article should be addressed to Jeff W. Johnson, CEB, 15 Marcin Hill, Burnsville, MN 55337. E-mail: jejohnson@cebglobal.com

Extract

In their critical review of survey key driver analyses (SKDA), Cucina, Walmsley, Gast, Martin, and Curtin (2017) contend that methodological issues limit the usefulness of SKDA and recommend that survey providers stop conducting SKDA until these issues can be overcome. I contend that many of these methodological issues are either overstated or able to be addressed through the proper application of the technique by a competent professional. In this commentary, I make recommendations for how SKDA should be applied so that methodological issues are addressed and the value of SKDA is maximized. Many of these recommendations were made in Lundby and Johnson (2006), who were cited by Cucina et al. but did not have much impact on their focal article.

Type
Commentaries
Copyright
Copyright © Society for Industrial and Organizational Psychology 2017 

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References

Budescu, D. V. (1993). Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression. Psychological Bulletin, 114, 542551.Google Scholar
Cucina, J. M., Walmsley, P. T., Gast, I. F., Martin, N. R., & Curtin, P. (2017). Survey key driver analysis: Are we driving down the right road? Industrial and Organizational Psychology: Perspectives on Science and Practice, 10 (2), 234257.Google Scholar
Darlington, R. B. (1968). Multiple regression in psychological research and practice. Psychological Bulletin, 69, 161182.Google Scholar
Dawis, R. V., & Lofquist, L. H. (1984). A psychological theory of work adjustment. Minneapolis: University of Minnesota Press.Google Scholar
Gibson, W. A. (1962). Orthogonal predictors: A possible resolution of the Hoffman-Ward controversy. Psychological Reports, 11, 3234.CrossRefGoogle Scholar
Green, P. E., & Tull, D. S. (1975). Research for marketing decisions (3rd ed.). Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Green, P. E., Carroll, J. D., & DeSarbo, W. S. (1978). A new measure of predictor variable importance in multiple regression. Journal of Marketing Research, 15, 356360.CrossRefGoogle Scholar
Healy, M. J. R. (1990). Measuring importance. Statistics in Medicine, 9, 633637.Google Scholar
Hoffman, P. J. (1960). The paramorphic representation of clinical judgment. Psychological Bulletin, 57, 116131.Google Scholar
Holland, J. L. (1997). Making vocational choices: A theory of vocational personalities and work environments (3rd ed.). Odessa, FL: Psychological Assessment Resources.Google Scholar
Johnson, J. W. (2000). A heuristic method for estimating the relative weight of predictor variables in multiple regression. Multivariate Behavioral Research, 35, 119.CrossRefGoogle ScholarPubMed
Johnson, J. W. (2004). Factors affecting relative weights: The influence of sampling and measurement error. Organizational Research Methods, 7, 283299.CrossRefGoogle Scholar
Kruskal, W. (1987). Relative importance by averaging over orderings. American Statistician, 41, 610.Google Scholar
Lundby, K. M., & Johnson, J. W. (2006). Relative weights of predictors: What is important when many forces are operating. In Kraut, A. I. (Ed.), Getting action from organizational surveys: New concepts, methods, and applications (pp. 326351). San Francisco, CA: Jossey-Bass.Google Scholar
Maxwell, S. E. (2000). Sample size and multiple regression analysis. Psychological Methods, 5, 434458.CrossRefGoogle ScholarPubMed
Schneider, B. (1987). The people make the place. Personnel Psychology, 40, 437453.Google Scholar
Su, R., Murdock, C., & Rounds, J. (2015). Person-environment fit. In Hartung, P. J., Savickas, M. L., & Walsh, W. B. (Eds.), APA handbook of career intervention: Vol. 1. Foundations (pp. 8198). Washington, DC: American Psychological Association.CrossRefGoogle Scholar
Tett, R. P., Hundley, N., & Christiansen, N. D. (in press). Meta-analysis and the myth of generalizability. Industrial and Organizational Psychology: Perspectives on Science and Practice. doi: 10.1017/iop.2017.26 CrossRefGoogle Scholar