A Systematic Evaluation and Comparison Between Exploratory Structural Equation Modeling and Bayesian Structural Equation Modeling. Issue 4 (4th July 2019)

• Record Type: Journal Article
• Authors: Guo, Jiesi; Marsh, Herbert W.; Parker, Philip D.; Dicke, Theresa; Lüdtke, Oliver; Diallo, Thierno M. O.
• Is Part Of: Structural equation modeling. Volume 26:Issue 4(2019)
• Page Start: 529
• Page End: 556
• Publication Date: 2019-07-04
• Subjects: Factor analysis -- Bayesian statistics -- exploratory structural equation modeling -- informative priors; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2018.1554999
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : In this study, we contrast two competing approaches, not previously compared, that balance the rigor of CFA/SEM with the flexibility to fit realistically complex data. Exploratory SEM (ESEM) is claimed to provide an optimal compromise between EFA and CFA/SEM. Alternatively, a family of three Bayesian SEMs (BSEMs) replace fixed-zero estimates with informative, small-variance priors for different subsets of parameters: cross-loadings (CL), residual covariances (RC), or CLs and RCs (CLRC). In Study 1, using three simulation studies, results showed that (1) BSEM-CL performed more closely to ESEM; (2) BSEM-CLRC did not provide more accurate model estimation compared with BSEM-CL; (3) BSEM-RC provided unstable estimation; and (4) different specifications of targeted values in ESEM and informative priors in BSEM have significant impacts on model estimation. The real data analysis (Study 2) showed that the differences in estimation between different models were largely consistent with those in Study1 but somewhat smaller.