Session Information
09 SES 08 B, Theoretical and Methodological Issues in Testing and Measurement (Part 1)
Paper Session
Contribution
Structural equation modeling (SEM) is a statistical methodology that takes a hypothesis testing approach to the analysis of a structural theory (Raykov & Marcoulides, 2006). It is used as a technique for both construct validation and theory development (Pedhazur & Pedhazur, 1991). Nowadays, SEM has become increasingly popular among researchers from many different disciplines. Interest in SEM is evident by the growing number of software programs developed for analyzing SEM (such as AMOS, EQS, LISREL, and Mplus), numerous graduate level courses and continuing education workshops prepared for explaining SEM, and latest empirical studies where the researchers describe their SEM results (Kline, 2011).
In SEM, hypothesis testing consists of confirming that a theoretical specified model fits sample variance-covariance data, by testing the significance of structural coefficients or testing the equality of coefficients between groups (Schumacker & Lomax, 2004). The power of SEM hypothesis testing depends on “the true population model, significance level, degrees of freedom, and sample size” (Schumacker & Lomax, 2004, p.113). In this aspect, MacCallum, Brown and Sugawara (1996) suggested a method for calculating power of SEM studies, where power is considered in terms of a null and alternative value of the root-mean-square error of approximation (RMSEA) index. In this study, a meta-analysis was performed to determine statistical power of research studies conducted with Structural Equation Modeling technique. MacCallum, Browne and Sugawara’s (1996) method was used for the power analyses.
Method
Expected Outcomes
References
Cohen, J. (1992). Quantitative methods in psychology: A power primer. Psychological Bulletin, 112(1), 155-159. Kline, R.B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: The Guilford Press. MacCallum, R.C., Browne, M.W., & Sugawara, H.M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130-149. Pedhazur, E. J. & Pedhazur-Schmelkin, L.P. (1991). Measurement, design, and analysis: An integrated approach. Hillsdale, NJ: Lawrence Earlbawm Associate. Raykov, T., & Marcoulides, G.A. (2006). A first course in structural equation modeling (2nd Edition). Lawrence Erlbaum Associates, Inc. Publishers. Schumacker, R.E., & Lomax, R.G. (2004). A beginner’s guide to structural equation modeling. Mahwah, NJ: Lawrence Erlbaum.
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