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The issue of statistical power for overall model fit in evaluating structural equation models

Author

Listed:
  • Richard HERMIDA

    () (George Mason University, 3575 Owasso Street, Shoreview, MN, USA, 55126)

  • Joseph N. LUCHMAN

    () (George Mason University, 4400 University Drive, Fairfax, VA, USA, 22030)

  • Vias NICOLAIDES

    () (George Mason University, 4400 University Drive, Fairfax, VA, USA, 22030)

  • Cristina WILCOX

    () (George Mason University, 4400 University Drive, Fairfax, VA, USA, 22030)

Abstract

Statistical power is an important concept for psychological research. However, examining the power of a structural equation model (SEM) is rare in practice. This article provides an accessible review of the concept of statistical power for the Root Mean Square Error of Approximation (RMSEA) index of overall model fit in structural equation modeling. By way of example, we examine the current state of power in the literature by reviewing studies in top Industrial - Organizational (I/O) Psychology journals using SEMs. Results indicate that in many studies, power is very low, which implies acceptance of invalid models. Additionally, we examined methodological situations which may have an influence on statistical power of SEMs. Results showed that power varies significantly as a function of model type and whether or not the model is the main model for the study. Finally, results indicated that power is significantly related to model fit statistics used in evaluating SEMs. The results from this quantitative review imply that researchers should be more vigilant with respect to power in structural equation modeling. We therefore conclude by offering methodological best practices to increase confidence in the interpretation of structural equation modeling results with respect to statistical power issues.

Suggested Citation

  • Richard HERMIDA & Joseph N. LUCHMAN & Vias NICOLAIDES & Cristina WILCOX, 2015. "The issue of statistical power for overall model fit in evaluating structural equation models," Computational Methods in Social Sciences (CMSS), "Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences, vol. 3(1), pages 25-42, June.
  • Handle: RePEc:ntu:ntcmss:vol3-iss1-15-025
    as

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    References listed on IDEAS

    as
    1. McQuitty, Shaun, 2004. "Statistical power and structural equation models in business research," Journal of Business Research, Elsevier, vol. 57(2), pages 175-183, February.
    2. Le, Huy & Schmidt, Frank L. & Harter, James K. & Lauver, Kristy J., 2010. "The problem of empirical redundancy of constructs in organizational research: An empirical investigation," Organizational Behavior and Human Decision Processes, Elsevier, vol. 112(2), pages 112-125, July.
    3. P. M. Bentler & Chih-Ping Chou, 1987. "Practical Issues in Structural Modeling," Sociological Methods & Research, , vol. 16(1), pages 78-117, August.
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