Bayesian Structural Equations Modeling for Ordinal Response Data with Missing Responses and Missing Covariates

Sungduk Kim, Sonali Das, Ming-Hui Chen and Nicholas Warren

Communications in Statistics - Theory and Methods, 2009, vol. 38, issue 16-17, 2748-2768

Abstract: Structural equations models (SEMs) have been extensively used to model survey data arising in the fields of sociology, psychology, health, and economics with increasing applications where self assessment questionnaires are the means to collect the data. We propose the SEM for multilevel ordinal response data from a large multilevel survey conducted by the US Veterans Health Administration (VHA). The proposed model involves a set of latent variables to capture dependence between different responses, a set of facility level random effects to capture facility heterogeneity and dependence between individuals within the same facility, and a set of covariates to account for individual heterogeneity. An effective and practically useful modeling strategy is developed to deal with missing responses and to model missing covariates in the structural equations framework. A Markov chain Monte Carlo sampling algorithm is developed for sampling from the posterior distribution. The deviance information criterion measure is used to compare several variations of the proposed model. The proposed methodology is motivated and illustrated by using the VHA All Employee Survey data.

Date: 2009
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610910902936299 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:38:y:2009:i:16-17:p:2748-2768

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610910902936299

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().