Events and Meetings of Italian Statistical Society, Advances in Latent Variables - Methods, Models and Applications

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A nonparametric multidimensional latent class IRT model in a Bayesian framework
Francesco Bartolucci, Alessio Farcomeni, Luisa Scaccia

Last modified: 2013-06-14

Abstract


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.

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