A Systematic Evaluation and Comparison Between Exploratory Structural Equation Modeling and Bayesian Structural Equation Modeling.

Autor/inn/en	Guo, Jiesi; Marsh, Herbert W.; Parker, Philip D.; Dicke, Theresa; Lüdtke, Oliver; Diallo, Thierno M. O.
Titel	A Systematic Evaluation and Comparison Between Exploratory Structural Equation Modeling and Bayesian Structural Equation Modeling.
Quelle	In: Structural equation modeling, 26 (2019) 4, S. 529-556 PDF als Volltext Link als defekt meldenVerfügbarkeit
Sprache	englisch
Dokumenttyp	online; gedruckt; Zeitschriftenaufsatz
ISSN	1070-5511; 1532-8007
DOI	10.1080/10705511.2018.1554999
Schlagwörter	Evaluation; Faktorenanalyse; Strukturformel; Strukturmodell; Messgenauigkeit; Bayes-Statistik; Statistik; Modellierung; Priorität
Abstract	In this study, [the authors] 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 Study 1 but somewhat smaller. (Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2021/2