Bayesian Analysis for Mixtures of Continuous, Ordinal and Nominal Repeated Measures
Thomas R Belin, The University of California-Los Angeles
John Boscardin, UCLA
*Xiao Zhang, The University of Alabama at Birmingham
Keywords: multivariate probit model
From a Bayesian perspective, we propose a unified model for analyzing mixtures of continuous, ordinal and nominal repeated measures, which is an unity of multivariate normal linear regression models for repeated continuous variables, multivariate probit models for repeated ordinal variables and multivariate multinomial probit models for repeated nominal variables. Since both the multivariate probit model and the multivariate multinomial probit model assume underlying normal variables for each ordinal and nominal variable, respectively, we combine the multivariate normal variables for continuous data and the underlying normal variables from the multivariate probit model and the multivariate multinomial probit model. Our model and algorithm are illustrated through simulated examples and an application to a foreign language study.