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A Shadow-Test Approach to Adaptive Item Calibration

Author

Listed:
  • Wim J. van der Linden

    (University of Twente)

  • Bingnan Jiang

    (ACT, Inc.)

Abstract

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 $$D_{\mathrm{s}}$$ D s -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.

Suggested Citation

  • Wim J. van der Linden & Bingnan Jiang, 2020. "A Shadow-Test Approach to Adaptive Item Calibration," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 301-321, June.
    • Handle: RePEc:spr:psycho:v:85:y:2020:i:2:d:10.1007_s11336-020-09703-8
    DOI: 10.1007/s11336-020-09703-8
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    References listed on IDEAS

    as
    1. Wim Linden & Hao Ren, 2015. "Optimal Bayesian Adaptive Design for Test-Item Calibration," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 263-288, June.
    2. Wim J. van der Linden & Hao Ren, 2020. "A Fast and Simple Algorithm for Bayesian Adaptive Testing," Journal of Educational and Behavioral Statistics, , vol. 45(1), pages 58-85, February.
    3. Hao Ren & Wim J. van der Linden & Qi Diao, 2017. "Continuous Online Item Calibration: Parameter Recovery and Item Utilization," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 498-522, June.
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