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An Item Response Model for Characterizing Test Compromise

Author

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  • Daniel O. Segall

Abstract

This article presents an item response model for characterizing test-compromise that enables the estimation of item-preview and score-gain distributions observed in on-demand high-stakes testing programs. Model parameters and posterior distributions are estimated by Markov Chain Monte Carlo (MCMC) procedures. Results of a simulation study suggest that when at least some of the items taken by a small sample of test takers are known to be secure (uncompromised), the procedure can provide useful summaries of test-compromise and its impact on test scores. The article includes discussions of operational use of the proposed procedure, possible model violations and extensions, and application to computerized adaptive testing.

Suggested Citation

  • Daniel O. Segall, 2002. "An Item Response Model for Characterizing Test Compromise," Journal of Educational and Behavioral Statistics, , vol. 27(2), pages 163-179, June.
    • Handle: RePEc:sae:jedbes:v:27:y:2002:i:2:p:163-179
    DOI: 10.3102/10769986027002163
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    Cited by:

    1. Chun Wang & Gongjun Xu & Zhuoran Shang, 2018. "A Two-Stage Approach to Differentiating Normal and Aberrant Behavior in Computer Based Testing," Psychometrika, Springer;The Psychometric Society, vol. 83(1), pages 223-254, March.

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