Download or read book A Comparison of Frequentist and Bayesian Approaches for Confirmatory Factor Analysis written by Menglin Xu and published by . This book was released on 2019 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model fit indices within the framework of structural equation models are crucial in evaluating and selecting the most appropriate model to fit data. The performance of fit indices under varying suboptimal conditions requires further investigation. Moreover, with the increasing interest in applying Bayesian method to social sciences data, the comparison of Bayesian estimation and robust maximum likelihood (MLR) estimation methods in evaluating models and estimating parameters is of vital importance. This study aims 1 ) to investigate the performance of MLR associated model fit indices under various conditions of model misfit, data distribution, and sample sizes; 2) to compare the performance of Bayesian and MLR methods in model fit and parameter estimation based on a confirmatory factor analysis (CFA) model. Data were simulated based on a population CFA model consistent with Curran, West and Finch’s (1996) study using R 3.4.0. Simulation conditions include 3 sample sizes (N = 200, 500, 1000), 3 degrees of model misfit (none: RMSEA = 0; mild: RMSEA = .05; moderate: RMSEA = .10), and 3 degrees of data nonnormality (normal: skewness = 0, kurtosis = 0; mild: skewness = 1, kurtosis = 3; moderate: skewness = 2, kurtosis = 7). Model misfit was introduced using Cudeck and Browne’s (1992) method through the R package MBESS. Data were fit using the R package lavaan for MLR method and blavaan for Bayesian method. Results show that scaled CFI and scaled TLI are the most robust model fit indices to variousiii suboptimal conditions; compared to p values associated with MLR, PP p values associated with the Bayesian method are robust to small sample size and data nonnormality under correctly specified models, less sensitive to models with ignorable degree of misfit, and have sufficient statistical power to reject moderately misspecified models; Bayesian and MLR methods have similar performance in point estimation; MLR method produces more robust standard error estimations. Implications and suggestions for future students are discussed.