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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 ,
Michael P. Wilmot  and
Jack W. Kostal
Brenton M. Wiernik*
Affiliation:
Department of Psychology, University of Minnesota
Michael P. Wilmot
Affiliation:
Department of Psychology, University of Minnesota
Jack W. Kostal
Affiliation:
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: wiernik@workpsy.ch
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Extract

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.

Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 8 , Issue 3 , September 2015 , pp. 438 - 445
Copyright
Copyright © Society for Industrial and Organizational Psychology 2015 

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