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The First Principal Component of Multifaceted Variables: It's More Than a G Thing

Published online by Cambridge University Press:  02 October 2015

Duncan J. R. Jackson ,
Dan J. Putka  and
Kevin R. H. Teoh
Duncan J. R. Jackson*
Affiliation:
Department of Organizational Psychology, Birkbeck, University of London, and Faculty of Management, University of Johannesburg
Dan J. Putka
Affiliation:
Human Resources Research Organization, Alexandria, Virginia
Kevin R. H. Teoh
Affiliation:
Department of Organizational Psychology, Birkbeck, University of London
*
Correspondence concerning this article should be addressed to Duncan J. R. Jackson, Department of Organizational Psychology, Birkbeck, University of London, Clore Management Centre, Torrington Square, London, United KingdomWC1E 7JL. E-mail: [email protected]
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Extract

Ree, Carretta, and Teachout (2015) raise the need for further investigation into dominant general factors (DGFs) and their prevalence in measures used for the purposes of employee selection, development, and performance measurement. They imply that a method of choice for estimating the contribution of DGFs is principal components analysis (PCA), and they interpret the variance accounted for by the first component of the PCA solution as indicative of the contribution of a general factor. In this response, we illustrate the hazard of equating the first component of a PCA with a general factor, and we illustrate how this becomes particularly problematic when applying PCA to multifaceted variables. Rather than simply critique this use of PCA, we offer an alternative approach that helps to address and illustrate the problem that we raise.

Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 8 , Issue 3 , September 2015 , pp. 446 - 452
Copyright
Copyright © Society for Industrial and Organizational Psychology 2015 

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