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.

近年来，在个体主观有序选择数据的分析中，纳入个体间异质性的模型变得越来越流行。但是，以前很少有研究在多元有序概率模型中包含个体异质性。在本文中，我们描述了Chen和Dey引入的贝叶斯多元有序概率模型（在Dey，Ghosh，Malrick（编）中，广义线性模型：贝叶斯的观点。Marcel-Dekker，纽约，第133–157页，2000年） （算法1），并提出了一种新的算法，该算法在割点函数中包括个体异质性（算法2）。此外，我们使用2005年至2009年收集的世界价值调查第5轮的真实数据研究了这两种算法。