Bayesian estimation of a multilevel IRT model using gibbs sampling

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In this article, a two-level regression model is imposed on the ability parameters in an item response theory (IRT) model. The advantage of using latent rather than observed scores as dependent variables of a multilevel model is that it offers the possibility of separating the influence of item difficulty and ability level and modeling response variation and measurement error. Another advantage is that, contrary to observed scores, latent scores are test-independent, which offers the possibility of using results from different tests in one analysis where the parameters of the IRT model and the multilevel model can be concurrently estimated. The two-parameter normal ogive model is used for the IRT measurement model. It will be shown that the parameters of the two-parameter normal ogive model and the multilevel model can be estimated in a Bayesian framework using Gibbs sampling. Examples using simulated and real data are given.
Original languageEnglish
Pages (from-to)271-288
Number of pages17
Issue number2
Publication statusPublished - 2001


  • Bayes estimates
  • Gibbs sampler
  • Item response theory (IRT)
  • Markov chain
  • Monte Carlo
  • Multilevel models (MLMs)
  • Two-parameter normal ogive model


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