Bayesian estimation of Thurstonian ranking models based on the Gibbs sampler

This paper presents a Gibbs sampler for the estimation of Thurstonian ranking models. This approach is useful for the analysis of ranking data with a large number of options. Approaches for assessing the goodness‐of‐fit of Thurstonian ranking models based on posterior predictive distributions are also discussed. Two simulation studies and two ranking studies are presented to illustrate that the Gibbs sampler is a promising solution to the numerical problems that previously plagued the estimation of Thurstonian ranking models.