title: Stochastic EM for estimating the parameters of a multilevel IRT model.
abstract: 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.
authors.affiliation: Department of Educational Measurement and Data Analysis, University of Twente, 7500 AE Enschede, The Netherlands. j.p.fox@utwente.nl
authors.forename: J-P
authors.initials: JP
authors.lastname: Fox
mesh_terms: D004522:Educational Status; D006801:Humans; D008960:Models, Psychological; D011584:Psychology / Q000379:methods* / Q000706:statistics & numerical data*; D012494:Sampling Studies; D013269:Stochastic Processes