On the Performance of Maximum Likelihood Versus Means and Variance Adjusted Weighted Least Squares Estimation in CFA André Beauducel Mannheim University Philipp Yorck Herzberg Technical University Dresden The simulation study compared maximum likelihood (ML) estimation with weighted least squares means and variance adjusted (WLSMV) estimation. The sim- ulation study was based on confirmatory factor analyses with 1, 2, 4, and 8 factors, based on 250, 500, 750, and 1,000 cases, and on 5, 10, 20, and 40 variables with 2, 3, 4, 5, and 6 categories. There was no model misspecification. The most important re- sults were that with 2 and 3 categories the rejection rates of the WLSMV chi-square test corresponded much more to the expected rejection rates according to an alpha level of .05 than the rejection rates of the ML chi-square test. The magnitude of the loadings was more precisely estimated by means of WLSMV when the variables had only 2 or 3 categories. The sample size for WLSMV estimation needed not to be larger than the sample size for ML estimation. Maximum likelihood (ML) estimation is most commonly used in confirmatory factor analysis (CFA) and structural equation modeling (SEM). ML estimation as- sumes that the observed variables follow the multivariate normal distribution. However, many data in psychological research are ordinal data or do not follow the multivariate normal distribution. One approach for analyzing ordinal data is weighted least squares (WLS) estimation, which assumes that the observed ordinal STRUCTURAL EQUATION MODELING, 13(2), 186–203 Copyright © 2006, Lawrence Erlbaum Associates, Inc. Correspondence should be addressed to Dr. André Beauducel, Department of Psychology II, Mannheim University, Schloss, Ehrenhof Ost, 68131 Mannheim, Germany. E-mail: beauducel@ tnt.psychologie.uni-mannheim.de