Polytomous Item Explanatory Item Response Theory Models

Jinho Kim, Mark Wilson

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

This study investigates polytomous item explanatory item response theory models under the multivariate generalized linear mixed modeling framework, using the linear logistic test model approach. Building on the original ideas of the many-facet Rasch model and the linear partial credit model, a polytomous Rasch model is extended to the item location explanatory many-facet Rasch model and the step difficulty explanatory linear partial credit model. To demonstrate the practical differences between the two polytomous item explanatory approaches, two empirical studies examine how item properties explain and predict the overall item difficulties or the step difficulties each in the Carbon Cycle assessment data and in the Verbal Aggression data. The results suggest that the two polytomous item explanatory models are methodologically and practically different in terms of (a) the target difficulty parameters of polytomous items, which are explained by item properties; (b) the types of predictors for the item properties incorporated into the design matrix; and (c) the types of item property effects. The potentials and methodological advantages of item explanatory modeling are discussed as well.

Original languageEnglish
Pages (from-to)726-755
Number of pages30
JournalEducational and Psychological Measurement
Volume80
Issue number4
DOIs
StatePublished - 1 Aug 2020

Keywords

  • explanatory item response models
  • item property
  • linear logistic test model
  • linear partial credit model
  • many-facet Rasch model
  • polytomous item

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