Factor analysis models with continuous and ordinal responses are a useful tool for assessing relations between the latent\nvariables and mixed observed responses. These models have been successfully applied to many different fields, including\nbehavioral, educational, and social-psychological sciences. However, within the Bayesian analysis framework, most developments\nare constrained within parametric families, of which the particular distributions are specified for the parameters of interest. This\nleads to difficulty in dealing with outliers and/or distribution deviations. In this paper, we propose a Bayesian semiparametric\nmodeling for factor analysis model with continuous and ordinal variables. A truncated stick-breaking prior is used to model\nthe distributions of the intercept and/or covariance structural parameters. Bayesian posterior analysis is carried out through\nthe simulation-based method. Blocked Gibbs sampler is implemented to draw observations from the complicated posterior. For\nmodel selection, the logarithm of pseudomarginal likelihood is developed to compare the competing models. Empirical results are\npresented to illustrate the application of the methodology.
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