Bayesian analysis of dynamic item response models in educational testing
Xiaojing Wang, James O. Berger, Donald S. Burdick
The Annals of Applied Statistics · 2013 · Volume 7 · Issue 1
Abstract
Item response theory (IRT) models have been widely used in educational measurement testing. When there are repeated observations available for individuals through time, a dynamic structure for the latent trait of ability needs to be incorporated into the model, to accommodate changes in ability. Other complications that often arise in such settings include a violation of the common assumption that test results are conditionally independent, given ability and item difficulty, and that test item difficulties may be partially specified, but subject to uncertainty. Focusing on time series dichotomous response data, a new class of state space models, called Dynamic Item Response (DIR) models, is proposed. The models can be applied either retrospectively to the full data or on-line, in cases where real-time prediction is needed. The models are studied through simulated examples and applied to a large collection of reading test data obtained from MetaMetrics, Inc.
Keywords
dynamic linear models, forward filtering and backward sampling, Gibbs sampling, IRT models, local dependence, random effects
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