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Statistical Foundations for Computerized Adaptive Testing with Response Revision.

Shiyu Wang1, Georgios Fellouris2, Hua-Hua Chang3

  • 1University of Georgia, Athens, USA. swang44@uga.edu.

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|February 27, 2019
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Summary
This summary is machine-generated.

Computerized adaptive testing (CAT) now allows response revision, enhancing test-taker flexibility without sacrificing statistical efficiency. This study extends existing methods to more item types and strategies.

Keywords:
computerized adaptive testingdichotomous IRT modelsitem response theory (IRT)large sample propertypolytomous IRT modelsresponse revision

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • The integration of response revision into computerized adaptive testing (CAT) presents a long-standing challenge in psychometrics.
  • Balancing test-taker agency (allowing answer changes) with test integrity (preventing deceptive strategies) and maintaining CAT's statistical efficiency is crucial.

Purpose of the Study:

  • To reformulate and extend an existing estimation approach for CAT with response revision.
  • To incorporate a broader range of item response theory (IRT) models, including polytomous and dichotomous items.
  • To investigate the performance of this extended approach under various test-taking strategies via simulation.

Main Methods:

  • The study builds upon the nominal response model-based estimation approach proposed by Wang et al. (2017).
  • The approach is extended to accommodate a wider array of dichotomous and polytomous IRT models.
  • Simulation studies are employed to evaluate the method's efficacy across different response revision strategies.

Main Results:

  • The reformulated approach successfully integrates response revision within CAT across various IRT models.
  • Simulation results indicate the method's robustness under different test-taking behaviors.
  • The statistical efficiency of CAT is preserved while offering test-takers the opportunity to revise answers.

Conclusions:

  • The extended estimation approach provides a viable solution for implementing response revision in CAT.
  • This methodology enhances the user experience by allowing answer changes without compromising measurement accuracy.
  • Further research can explore real-world implementation and its impact on test fairness and validity.