A Shadow-Test Approach to Adaptive Item Calibration

Wim J. van der Linden*, Bingnan Jiang

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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A shadow-test approach to the calibration of field-test items embedded in adaptive testing is presented. The objective function used in the shadow-test model selects both the operational and field-test items adaptively using a Bayesian version of the criterion of Ds-optimality. The constraint set for the model can be used to hide the field-test items completely in the content of the test as well as to deal with such practical issues as random control of their exposure rates. The approach runs on efficient implementations of the Gibbs sampler for the real-time updating of the ability and field-test parameters. Optimal settings for the proposed algorithms were found and used to demonstrate item calibration with smaller than traditional sample sizes in runtimes fully comparable with conventional adaptive testing.

Original languageEnglish
Pages (from-to)301-321
Number of pages21
Issue number2
Publication statusPublished - 17 Jun 2020


  • UT-Hybrid-D
  • Bayesian D-optimality
  • Gibbs sampling
  • item calibration
  • item response models
  • MCMC algorithm
  • shadow-test approach
  • adaptive testing

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