A Bayesian Approach for Analyzing Hierarchical Data with Missing Outcomes through Structural Equation Models

Autor/inn/en	Song, Xin-Yuan; Lee, Sik-Yum
Titel	A Bayesian Approach for Analyzing Hierarchical Data with Missing Outcomes through Structural Equation Models
Quelle	In: Structural Equation Modeling: A Multidisciplinary Journal, 15 (2008) 2, S.272-300 (29 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1070-5511
Schlagwörter	Psychological Studies; Life Satisfaction; Job Satisfaction; Structural Equation Models; Simulation; Bayesian Statistics; Computer Software; Comparative Analysis; Foreign Countries + Suchen Sie Ihr Suchwort? Lebensvollendung; Labor; Labour; Satisfaction; Arbeit; Zufriedenheit; Simulation program; Simulationsprogramm; Ausland
Abstract	Structural equation models are widely appreciated in behavioral, social, and psychological research to model relations between latent constructs and manifest variables, and to control for measurement errors. Most applications of structural equation models are based on fully observed data that are independently distributed. However, hierarchical data with a correlated structure are common in behavioral research, and very often, missing data are encountered. In this article, we propose a 2-level structural equation model for analyzing hierarchical data with missing entries, and describe a Bayesian approach for estimation and model comparison. We show how to use WinBUGS software to get the solution conveniently. The proposed methodologies are illustrated through a simulation study, and a real application in relation to organizational and management research concerning the study of the interrelationships of the latent constructs about job satisfaction, job responsibility, and life satisfaction for citizens in 43 countries. (Contains 3 figures and 3 tables.) (Author).
Anmerkungen	Lawrence Erlbaum. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals/default.html
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10