Model comparison of Bayesian structural equation models with mixed ordered categorical and dichotomous data
The purpose of this paper is to describe the mixed variables (ordered categorical and dichotomous) in Bayesian structural equation models. Markov chain Monte Carlo simulation (MCMC) via Gibbs sampling method is applied for estimation the parameters. Statistical analyses, which include parameters est...
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| Main Authors: | , , |
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| Format: | Article |
| Published: |
Taylor & Francis Group, LLC
2017
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/80877/ https://www.tandfonline.com/doi/abs/10.1080/09720510.2016.1238111 |
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| Summary: | The purpose of this paper is to describe the mixed variables (ordered categorical and dichotomous) in Bayesian structural equation models. Markov chain Monte Carlo simulation (MCMC) via Gibbs sampling method is applied for estimation the parameters. Statistical analyses, which include parameters estimation, standard error, higest posterior density and Devience information creterion for testing the prposed models, are discussed. Hidden continuous normal distribution with censoring is used to handle the problem of mixed variables (ordered categorical and dichotomous). Comparison between Bayesian linear and non-linear SEMs are discussed. The proposed models are illustrated by a case study for breast cancer patient’s which obtained from the hospital. Analyses are done by using WinBUGS program. The results showed that the results of non-linear Bayesian SEM is better than the results of linear Bayesian SEM. |
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