Polytomous Item Response Theory Models, Issue 144
Polytomous Item Response Theory Models provides a unified, comprehensive introduction to the range of polytomous models available within item response theory (IRT). It begins by outlining the primary structural distinction between the two major types of polytomous IRT models. This focuses on the two types of response probability that are unique to polytomous models and their associated response functions, which are modeled differently by the different types of IRT model. It describes, both conceptually and mathematically, the major specific polytomous models, including the Nominal Response Model, the Partial Credit Model, the Rating Scale model, and the Graded Response Model.
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Analysis Andersen Andrich Applied Psychological Measurement approach Baker cate category boundary category coefficient category g category scores CBRFs class of models context cumulative boundary defined described developed dichotomous items dichotomous model dichotomous Rasch model estimated example item framework given category gory GPCM Hambleton heterogeneous class ICRFs item category item information item location parameter item parameters item response theory item's latent class analysis latent class model latent trait logistic Masters & Wright measurement theory Muraki normal ogive number of categories obtained ordered categories ordered polytomous parameter values partial credit model polytomous IRT models polytomous item polytomous models polytomous Rasch models probability of responding provides rating scale model represents responding in category response categories response options response probabilities Samejima scoring function formulation specific models sufficient statistics Thissen threshold discrimination Thurstone Thurstone scaling tion trait continuum trait level vary across items Wright & Masters
Page 99 - Andrich, D. (1995) Models for measurement: Precision and the non-dichotomization of graded responses Psychometrika, 60, 7-26.
Page 99 - Test theory with qualitative and quantitative latent variables. In R. Langeheine & J. Rost (Eds.), Latent traits and latent class models (pp.
Page 96 - ANDRICH, D. (1979). A model for contingency tables having an ordered response classification. Biometrics, 35, 403-415. ANDRICH, D. (1982). An extension of the Rasch model for ratings providing both location and dispersion parameters.