Bayesian adaptive testing with polytomous items

Hao Ren, Seung W. Choi, Wim J. van der Linden*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)
45 Downloads (Pure)


An extremely efficient MCMC method for Bayesian adaptive testing with polytomous items is explained both conceptually and in mathematical detail. Results from extensive simulation studies with different item pools, polytomous response models, calibration sample sizes, and test lengths are presented. In addition, the case of adaptive testing from pools with a mixture of dichotomous and polytomous items is addressed.

Original languageEnglish
Pages (from-to)427-449
Number of pages23
Issue number2
Early online date21 May 2020
Publication statusPublished - 1 Jul 2020


  • UT-Hybrid-D
  • Bayesian optimality
  • Item calibration
  • MCMC algorithm
  • Polytomous response models
  • Adaptive testing


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