A nonparametric multidimensional latent class IRT model in a Bayesian framework

We propose a nonparametric Item Response Theory model for dichotomously scored items in a Bayesian framework. Partitions of the items are defined on the basis of inequality constraints among the latent class success probabilities. A Reversible Jump type algorithm is described for sampling from the posterior distribution. A consequence is the possibility to make inference on the number of dimensions (i.e., number of groups of items measuring the same latent trait) and to cluster items when unidimensionality is violated.