Reliable and More Powerful Methods for Power Analysis in Structural Equation Modeling. Issue 3 (4th May 2017)

• Record Type: Journal Article
• Authors: Yuan, Ke-Hai; Zhang, Zhiyong; Zhao, Yanyun
• Is Part Of: Structural equation modeling. Volume 24:Issue 3(2017)
• Page Start: 315
• Page End: 330
• Publication Date: 2017-05-04
• Subjects: likelihood ratio statistic -- Monte Carlo -- power -- robust estimation -- Type I errors; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2016.1276836
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : The normal-distribution-based likelihood ratio statistic is widely used for power analysis in structural Equation modeling (SEM). In such an analysis, power and sample size are computed by assuming that follows a central chi-square distribution under and a noncentral chi-square distribution under . However, with either violation of normality or not a large enough sample size, both empirical and analytical results indicate that the chi-square distribution assumptions are not realistic and consequently methods of power analysis based on such assumptions are not valid. This article describes a Monte Carlo (MC) method for power analysis. A measure of effect size for characterizing the power property of different rescaled statistics is also provided. Robust methods are proposed to increase the power of and other statistics. Simulation results show that the MC method reliably controls Type I errors and robust estimation methods effectively increase the power, and their combination is thus recommended for conducting power analysis in SEM.