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How Data Analysis Can Dominate Interpretations of Dominant General Factors

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  • Wiernik, Brenton M.
  • Wilmot, Michael P.
  • Kostal, Jack W.

Abstract

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.

Suggested Citation

  • Wiernik, Brenton M. & Wilmot, Michael P. & Kostal, Jack W., 2015. "How Data Analysis Can Dominate Interpretations of Dominant General Factors," Industrial and Organizational Psychology, Cambridge University Press, vol. 8(3), pages 438-445, September.
    • Handle: RePEc:cup:inorps:v:8:y:2015:i:03:p:438-445_00
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    Cited by:

    1. Charley Xia & Sarah J. Pickett & David C. M. Liewald & Alexander Weiss & Gavin Hudson & W. David Hill, 2023. "The contributions of mitochondrial and nuclear mitochondrial genetic variation to neuroticism," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Piotr Koc, 2021. "Measuring Non-electoral Political Participation: Bi-factor Model as a Tool to Extract Dimensions," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(1), pages 271-287, July.
    3. Michael D Coovert & Winston Bennett Jr, 2022. "The importance of identifying the dimensionality of constructs employed in simulation and training for AI," The Journal of Defense Modeling and Simulation, , vol. 19(2), pages 229-236, April.

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