A comparison of CFA, ESEM, and BSEM in test structure analysis
Publication in refereed journal


摘要Minor cross-loadings on non-targeted factors are often found in psychological or other instruments. Forcing them to zero in confirmatory factor analyses (CFA) leads to biased estimates and distorted structures. Alternatively, exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM) have been proposed. In this research, we compared the performance of the traditional independent-clusters-confirmatory-factor-analysis (ICM-CFA), the nonstandard CFA, ESEM with the Geomin- or Target-rotations, and BSEMs with different cross-loading priors (correct; small- or large-variance priors with zero mean) using simulated data with cross-loadings. Four factors were considered: the number of factors, the size of factor correlations, the cross-loading mean, and the loading variance. Results indicated that ICM-CFA performed the worst. ESEMs were generally superior to CFAs but inferior to BSEM with correct priors that provided the precise estimation. BSEM with large- or small-variance priors performed similarly while the prior mean for cross-loadings was more important than the prior variance.
著者Yue Xiao, Hongyun Liu, Kit-Tai Hau
期刊名稱Structural Equation Modeling: A Multidisciplinary Journal
頁次665 - 677
關鍵詞Bayesian structural equation modeling (BSEM), exploratory structural equation
modeling (ESEM), confirmatory factor analysis (CFA), prior specification

上次更新時間 2020-28-11 於 02:19