Bayesian Analysis of Structural Equation Models with Nonlinear Covariates and Latent Variables

Autor/inn/en	Song, Xin-Yuan; Lee, Sik-Yum
Titel	Bayesian Analysis of Structural Equation Models with Nonlinear Covariates and Latent Variables
Quelle	In: Multivariate Behavioral Research, 41 (2006) 3, S.337-365 (29 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0027-3171
DOI	10.1207/s15327906mbr4103_4
Schlagwörter	Structural Equation Models; Bayesian Statistics; Markov Processes; Monte Carlo Methods; Error of Measurement; Simulation; Computation; Research Methodology; Methods Research; Evaluation Methods; Demonstration Programs + Suchen Sie Ihr Suchwort? Markowscher Prozess; Monte-Carlo-Methode; Messfehler; Simulation program; Simulationsprogramm; Research method; Forschungsmethode; Methodenforschung
Abstract	In this article, we formulate a nonlinear structural equation model (SEM) that can accommodate covariates in the measurement equation and nonlinear terms of covariates and exogenous latent variables in the structural equation. The covariates can come from continuous or discrete distributions. A Bayesian approach is developed to analyze the proposed model. Markov chain Monte Carlo methods for obtaining Bayesian estimates and their standard error estimates, highest posterior density intervals, and a PP p value are developed. Results obtained from two simulation studies are reported to respectively reveal the empirical performance of the proposed Bayesian estimation in analyzing complex nonlinear SEMs, and in analyzing nonlinear SEMs with the normal assumption of the exogenous latent variables violated. The proposed methodology is further illustrated by a real example. Detailed interpretation about the interaction terms is presented. (Author).
Anmerkungen	Lawrence Erlbaum Associates, Inc. 10 Industrial Avenue, Mahwah, NJ 07430. Tel: 800-926-6579; Tel: 201-258-2200; Fax: 201-236-0072; e-mail: journals@erlbaum.com; Web site: https://www.erlbaum.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10