title: Bayesian Thurstonian models for ranking data using JAGS.
abstract: A Thurstonian model for ranking data assumes that observed rankings are consistent with those of a set of underlying continuous variables. This model is appealing since it renders ranking data amenable to familiar models for continuous response variables-namely, linear regression models. To date, however, the use of Thurstonian models for ranking data has been very rare in practice. One reason for this may be that inferences based on these models require specialized technical methods. These methods have been developed to address computational challenges involved in these models but are not easy to implement without considerable technical expertise and are not widely available in software packages. To address this limitation, we show that Bayesian Thurstonian models for ranking data can be very easily implemented with the JAGS software package. We provide JAGS model files for Thurstonian ranking models for general use, discuss their implementation, and illustrate their use in analyses.
authors.affiliation: Department of Statistical Science, University of Idaho, 875 Perimeter Drive Stop 1104, Moscow, ID, 83844-1104, USA. trjohns@uidaho.edu
authors.forename: Timothy R
authors.initials: TR
authors.lastname: Johnson
authors.forename: Kristine M
authors.initials: KM
authors.lastname: Kuhn
mesh_terms: D001499:Bayes Theorem; D035881:Compensation and Redress; D017047:Cost Sharing; D018864:Cultural Diversity; D007586:Job Application; D016014:Linear Models; D008137:Longitudinal Studies; D008960:Models, Psychological; D015233:Models, Statistical; D012945:Social Values; D012984:Software