A Nonparametric Multidimensional Latent Class IRT Model in a Bayesian Framework View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-09-12

AUTHORS

Francesco Bartolucci, Alessio Farcomeni, Luisa Scaccia

ABSTRACT

We propose a nonparametric item response theory model for dichotomously-scored items in a Bayesian framework. The model is based on a latent class (LC) formulation, and it is multidimensional, with dimensions corresponding to a partition of the items in homogenous groups that are specified on the basis of inequality constraints among the conditional success probabilities given the latent class. Moreover, an innovative system of prior distributions is proposed following the encompassing approach, in which the largest model is the unconstrained LC model. A reversible-jump type algorithm is described for sampling from the joint posterior distribution of the model parameters of the encompassing model. By suitably post-processing its output, we then make inference on the number of dimensions (i.e., number of groups of items measuring the same latent trait) and we cluster items according to the dimensions when unidimensionality is violated. The approach is illustrated by two examples on simulated data and two applications based on educational and quality-of-life data. More... »

PAGES

952-978

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11336-017-9576-7

DOI

http://dx.doi.org/10.1007/s11336-017-9576-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091612450

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28900804


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