Abstract
In recent years, models incorporating heterogeneity among individuals have become increasingly popular in the analyses on subjective ordered choice data. However, there are rare previous studies that include individual heterogeneity in the multivariate ordered probit model. In this article, we describe the Bayesian multivariate ordered probit model introduced by Chen and Dey (in: Dey, Ghosh, Mallick (eds) Generalized linear models: a Bayesian perspective. Marcel-Dekker, New York, pp 133–157, 2000) (Algorithm 1), and propose a new algorithm that includes individual heterogeneity in the cutpoint function (Algorithm 2). Further, we examine the two algorithms using real data from World Values Survey wave 5, collected between 2005 and 2009. The empirical results demonstrate that the model with individual heterogeneity outperforms that without heterogeneity.
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