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Bayesian estimation of a multilevel IRT model using Gibbs sampling (2001)
| Content Provider | CiteSeerX |
|---|---|
| Author | Fox, Jean-Paul Alas, Cees A. W. |
| Abstract | In this article, atwo-level regression model is imposed on the ability parameters in an item response theory (IRT) model. The advantage of using latent rather an 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 rror. 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 no mal ogive model is used for the IRT measurement model. It will be shown that he 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. |
| File Format | |
| Journal | Psychometrika |
| Language | English |
| Publisher Date | 2001-01-01 |
| Publisher Institution | University Of Twente |
| Access Restriction | Open |
| Subject Keyword | Bayesian Estimation Multilevel Model Multilevel Irt Model Observed Score Response Variation Measurement Rror Different Test Irt Model Mal Ogive Model Item Difficulty Irt Measurement Model Real Data Ability Level Two-parameter Normal Ogive Model Latent Score Item Response Theory Ability Parameter Gibbs Sampling Dependent Variable Bayesian Framework Atwo-level Regression Model |
| Content Type | Text |
| Resource Type | Article |
