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

Richard Hermida (), Joseph Luchman (), Vias Nicolaides () and Cristina Wilcox ()
Additional contact information
Richard Hermida: George Mason University, 3575 Owasso Street, Shoreview, MN, USA, 55126
Joseph 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

Computational Methods in Social Sciences (CMSS), 2015, vol. 3, issue 1, 25-42

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.

Keywords: statistical power; structural equation modeling; measurement; statistics; research methods. (search for similar items in EconPapers)
Date: 2015-06
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://cmss.univnt.ro/wp-content/uploads/vol/split ... _issue_1_art.003.pdf First version, 2015 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ntu:ntcmss:vol3-iss1-15-025

Access Statistics for this article

Computational Methods in Social Sciences (CMSS) is currently edited by Bogdan Oancea

More articles in Computational Methods in Social Sciences (CMSS) from "Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences Contact information at EDIRC.
Bibliographic data for series maintained by Stefan Ciucu ( this e-mail address is bad, please contact ).