  Typical IRT rating-scale models assume that the rating category threshold
parameters are the same over examinees. However, it can be argued that many
rating data sets violate this assumption. To address this practical
psychometric problem, we introduce a novel, Bayesian nonparametric IRT model
for rating scale items. The model is an infinite-mixture of Rasch partial
credit models, based on a localized Dependent Dirichlet process (DDP). The
model treats the rating thresholds as the random parameters that are subject to
the mixture, and has (stick-breaking) mixture weights that are
covariate-dependent. Thus, the novel model allows the rating category
thresholds to vary flexibly across items and examinees, and allows the
distribution of the category thresholds to vary flexibly as a function of
covariates. We illustrate the new model through the analysis of a simulated
data set, and through the analysis of a real rating data set that is well-known
in the psychometric literature. The model is shown to have better
predictive-fit performance, compared to other commonly used IRT rating models.
