Prior distributions for item parameters in IRT models

M. Matteucci, Prof. S. Mignani, Bernard P. Veldkamp

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)

Abstract

The focus of this article is on the choice of suitable prior distributions for item parameters within item response theory (IRT) models. In particular, the use of empirical prior distributions for item parameters is proposed. Firstly, regression trees are implemented in order to build informative empirical prior distributions. Secondly, model estimation is conducted within a fully Bayesian approach through the Gibbs sampler, which makes estimation feasible also with increasingly complex models. The main results show that item parameter recovery is improved with the introduction of empirical prior information about item parameters, also when only a small sample is available.
Original languageEnglish
Pages (from-to)2944-2958
Number of pages15
JournalCommunication in statistics : theory and methods
Volume41
Issue number16-17
DOIs
Publication statusPublished - 2012

Keywords

  • METIS-291694
  • IR-83904

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