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SKDA in Context

Published online by Cambridge University Press:  29 June 2017

William H. Macey*
Affiliation:
CultureFactors, Inc.
Diane L. Daum
Affiliation:
CEB
*
Correspondence concerning this article should be addressed to William H. Macey, CultureFactors, Inc., 175 N. Franklin St., Suite 401, Chicago, IL 60606. E-mail: wmacey@culturefactors.com, wmacey9@gmail.com

Extract

In contrast to the view that survey key driver analysis (SKDA) is a misused and blind empirical process, we suggest it is a reasonable, hypothesis-driven approach that builds on cumulative knowledge drawn from both the literature and practice, and requires reasoned judgment about the relationships of individual items to the constructs they represent and the criteria of interest. The logic of key driver analysis in applied settings is no different than the logic of its application in fundamental research regarding employee attitudes (e.g., Dalal, Baysinger, Brummel, & LeBreton, 2012). However, there are important survey design and analysis issues with respect to how key driver analyses are best conducted. Just some of these are discussed below.

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

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References

Bakker, A. B. (2011). An evidence-based model of work engagement. Current Directions in Psychological Science, 20 (4), 265269.CrossRefGoogle Scholar
Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: a practical information-theoretic approach (2nd ed.). New York: Springer Verlag.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.CrossRefGoogle Scholar
Dalal, R. S., Baysinger, M., Brummel, B. J., & LeBreton, J. M. (2012). The relative importance of employee engagement, other job attitudes, and trait affect as predictors of job performance. Journal of Applied Social Psychology, 42 (S1), E295–E325.CrossRefGoogle Scholar
Johnson, J. W. (2000). A heuristic method for estimating the relative weight of predictor variables in multiple regression. Multivariate Behavioral Research, 35 (1), 119.CrossRefGoogle ScholarPubMed
LeBreton, J. M., Hargis, M. B., Griepentrog, B., Oswald, F. L., & Ployhart, R. E. (2007). A multidimensional approach for evaluating variables in organizational research and practice. Personnel Psychology, 60 (2), 475498.CrossRefGoogle Scholar
Macey, W. H., & Bakker, A. B. (2012, April). Engaged employees in flourishing organizations. Preconference workshop presented at the 27th Annual Conference of the Society for Industrial and Organizational Psychology, San Diego, CA.Google Scholar
MacKenzie, S. B., Podsakoff, P. M., & Jarvis, C. B. (2005). The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. Journal of Applied Psychology, 90 (4), 710730.CrossRefGoogle ScholarPubMed
Saari, L. M., & Judge, T. A. (2004). Employee attitudes and job satisfaction. Human Resource Management, 43 (4), 395407.CrossRefGoogle Scholar
Tonidandel, S., & LeBreton, J. M. (2011). Relative importance analysis: A useful supplement to regression analysis. Journal of Business and Psychology, 26 (1), 19.CrossRefGoogle Scholar
Tonidandel, S., LeBreton, J. M., & Johnson, J. W. (2009). Determining the statistical significance of relative weights. Psychological Methods, 14 (4), 387399.CrossRefGoogle ScholarPubMed