Stochastic EM for estimating the parameters of a multilevel IRT model
An item response theory (IRT) model is used as a measurement error model for the dependent variable of a multilevel model. The dependent variable is latent but can be measured indirectly by using tests or questionnaires. The advantage of using latent scores as dependent variables of a multilevel mod...
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Published in | British journal of mathematical & statistical psychology Vol. 56; no. 1; pp. 65 - 81 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Oxford, UK
Blackwell Publishing Ltd
01.05.2003
British Psychological Society |
Subjects | |
Online Access | Get full text |
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Summary: | An item response theory (IRT) model is used as a measurement error model for the dependent variable of a multilevel model. The dependent variable is latent but can be measured indirectly by using tests or questionnaires. The advantage of using latent scores as dependent variables of a multilevel model is that it offers the possibility of modelling response variation and measurement error and separating the influence of item difficulty and ability level. The two‐parameter normal ogive model is used for the IRT model. It is shown that the stochastic EM algorithm can be used to estimate the parameters which are close to the maximum likelihood estimates. This algorithm is easily implemented. The estimation procedure will be compared to an implementation of the Gibbs sampler in a Bayesian framework. Examples using real data are given. |
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Bibliography: | ark:/67375/WNG-42VR6XHK-C istex:6C6BC27F7F337AE43BDA54DE475D74BF20377F7E ArticleID:BMSP100 ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2 |
ISSN: | 0007-1102 2044-8317 |
DOI: | 10.1348/000711003321645340 |