An evaluation of WLSMV and Bayesian methods for confirmatory factor analysis with categorical indicators Online publication date: Wed, 02-Jul-2014
by Xinya Liang; Yanyun Yang
International Journal of Quantitative Research in Education (IJQRE), Vol. 2, No. 1, 2014
Abstract: This Monte Carlo study evaluated the performance of three estimation methods in fitting confirmatory factor analysis (CFA) models for ordered categorical data, with the focus on data with non-normal underlying distributions and small sample sizes. The three methods are: weighted least squares with mean and variance adjusted (WLSMV), Bayesian with non-informative priors (BN), and Bayesian with informative priors (BI). Design factors included sample sizes, factor structures, underlying continuous distributions, and categorical distributions. Results were evaluated based on the model-data fit, point estimates, and standard errors of point estimates. Results showed that Bayesian methods encountered less convergence problems than WLSMV. Bayesian methods were robust to the non-normality of underlying continuous distributions. WLSMV tended to perform equally well or slightly better than Bayesian methods except for some conditions with small sample sizes or highly non-normal underlying distributions.
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