Kim, SDas, SonaliChen, M-HWarren, N2010-01-282010-01-282009-01Kim, S, Das, S et al. 2009. Bayesian structural equations modeling for ordinal response data with missing responses and missing covariates. Communications in statistics - Theory and methods, Vol.38(16-17), pp 2748 - 27680361-0926http://hdl.handle.net/10204/3920Copyright: 2009 Taylor & Francis. This is the pre print version of the work. It is posted here by permission of Taylor & Francis for your personal use. Not for redistribution. The definitive version was published in the Journal of Communications in statistics - Theory and methods, Vol.38(16-17), pp 2748 - 2768Structural 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 modelling 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.enDeviance information criteriaLatent variableMarkov chain Monte CarloOrdinal response dataRandom effectsVeterans health administrationArticleKim, S., Das, S., Chen, M., & Warren, N. (2009). Bayesian structural equations modeling for ordinal response data with missing responses and missing covariates. http://hdl.handle.net/10204/3920Kim, S, Sonali Das, M-H Chen, and N Warren "Bayesian structural equations modeling for ordinal response data with missing responses and missing covariates." (2009) http://hdl.handle.net/10204/3920Kim S, Das S, Chen M, Warren N. Bayesian structural equations modeling for ordinal response data with missing responses and missing covariates. 2009; http://hdl.handle.net/10204/3920.TY - Article AU - Kim, S AU - Das, Sonali AU - Chen, M-H AU - Warren, N AB - 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 modelling 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. DA - 2009-01 DB - ResearchSpace DP - CSIR KW - Deviance information criteria KW - Latent variable KW - Markov chain Monte Carlo KW - Ordinal response data KW - Random effects KW - Veterans health administration LK - https://researchspace.csir.co.za PY - 2009 SM - 0361-0926 T1 - Bayesian structural equations modeling for ordinal response data with missing responses and missing covariates TI - Bayesian structural equations modeling for ordinal response data with missing responses and missing covariates UR - http://hdl.handle.net/10204/3920 ER -