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How Data Analysis Can Dominate Interpretations of Dominant General Factors

Published online by Cambridge University Press:  02 October 2015

Brenton M. Wiernik*
Department of Psychology, University of Minnesota
Michael P. Wilmot
Department of Psychology, University of Minnesota
Jack W. Kostal
Department of Psychology, University of Minnesota
Correspondence concerning this article should be addressed to Brenton M. Wiernik, Department of Psychology, University of Minnesota, Minneapolis, MN 55455. E-mail:


A dominant general factor (DGF) is present when a single factor accounts for the majority of reliable variance across a set of measures (Ree, Carretta, & Teachout, 2015). In the presence of a DGF, dimension scores necessarily reflect a blend of both general and specific factors. For some constructs, specific factors contain little unique reliable variance after controlling for the general factor (Reise, 2012), whereas for others, specific factors contribute a more substantial proportion of variance (e.g., Kinicki, McKee-Ryan, Schriesheim, & Carson, 2002). We agree with Ree et al. that the presence of a DGF has implications for interpreting scores. However, we argue that the conflation of general and specific factor variances has the strongest implications for understanding how constructs relate to external variables. When dimension scales contain substantial general and specific factor variance, traditional methods of data analysis will produce ambiguous or even misleading results. In this commentary, we show how several common data analytic methods, when used with data sets containing a DGF, will substantively alter conclusions.

Copyright © Society for Industrial and Organizational Psychology 2015 

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Boker, S. M., Neale, M. C., Maes, H. H., Wilde, M. J., Spiegel, M., Brick, T. R., . . . Team OpenMx. (2015). OpenMx 2.0 user guide (Release No. 2.0.14157). Charlottesville, VA: University of Virginia. Retrieved from Scholar
Campbell, J. P., & Wiernik, B. M. (2015). The modeling and assessment of work performance. Annual Review of Organizational Psychology and Organizational Behavior, 2, 4774. Scholar
Chen, F. F., Hayes, A., Carver, C. S., Laurenceau, J.-P., & Zhang, Z. (2012). Modeling general and specific variance in multifaceted constructs: A comparison of the bifactor model to other approaches. Journal of Personality, 80, 219251. Scholar
Conway, J. M., & Lance, C. E. (2010). What reviewers should expect from authors regarding common method bias in organizational research. Journal of Business and Psychology, 25, 325334. Scholar
Edwards, B. D., Bell, S. T., Arthur, W. Jr., & Decuir, A. D. (2008). Relationships between facets of job satisfaction and task and contextual performance. Applied Psychology, 57, 441465. Scholar
Kinicki, A. J., McKee-Ryan, F. M., Schriesheim, C. A., & Carson, K. P. (2002). Assessing the construct validity of the Job Descriptive Index: A review and meta-analysis. Journal of Applied Psychology, 87, 1432. Scholar
MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological Methods, 4, 8499. Scholar
McAbee, S. T., Oswald, F. L., & Connelly, B. S. (2014). Bifactor models of personality and college student performance: A broad versus narrow view. European Journal of Personality, 28, 604619. Scholar
Ree, M. J., Carretta, T. R., & Teachout, M. S. (2015). Pervasiveness of dominant general factors in organizational measurement. Industrial and Organizational Psychology: Perspectives on Science and Practice, 8 (3), 409427.CrossRefGoogle Scholar
Reise, S. P. (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47, 667696. ScholarPubMed
Salgado, J. F., Moscoso, S., & Berges, A. (2013). Conscientiousness, its facets, and the prediction of job performance ratings: Evidence against the narrow measures. International Journal of Selection and Assessment, 21, 7484. Scholar
Wanous, J. P., Reichers, A. E., & Hudy, M. J. (1997). Overall job satisfaction: How good are single-item measures? Journal of Applied Psychology, 82, 247252. Scholar