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ERIC Number: EJ791591
Record Type: Journal
Publication Date: 2008-Apr
Pages: 29
Abstractor: Author
Reference Count: 38
ISSN: ISSN-1070-5511
A Bayesian Approach for Analyzing Hierarchical Data with Missing Outcomes through Structural Equation Models
Song, Xin-Yuan; Lee, Sik-Yum
Structural Equation Modeling: A Multidisciplinary Journal, v15 n2 p272-300 Apr 2008
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.)
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:
Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A