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We introduce a MCMC sampling for a generalized linear normal random effects model with the ordered probit link function based on latent variables from suitable truncated normal distribution. Such models have proven useful in practice and we have observed numerically reasonable results in the estimation of fixed effects when the random effect term is provided. Applications that utilize Korean Welfare Panel Study data can be difficult to model; subsequently, wefind that an ordered probit model with the random effects leads to an improved analyses with more accurate and precise inferences.

Korean Welfare Panel Data: A Computational Bayesian Method for Ordered Probit Random Effects Models