Statistical Foundations for Computerized Adaptive Testing with Response Revision

Shiyu Wang (), Georgios Fellouris and Hua-Hua Chang
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Shiyu Wang: University of Georgia
Georgios Fellouris: University of Illinois at Urbana-Champaign
Hua-Hua Chang: Purdue University

Psychometrika, 2019, vol. 84, issue 2, No 3, 375-394

Abstract: Abstract The compatibility of computerized adaptive testing (CAT) with response revision has been a topic of debate in psychometrics for many years. The problem is to provide test takers opportunities to change their answers during the test, while discouraging deceptive strategies from their side and preserving the statistical efficiency of the traditional CAT. The estimating approach proposed in Wang et al. (Stat Sin 27(4):1987–2010, 2017), based on the nominal response model, allows test takers to provide more than one answer to each item during the test, which they all contribute to the interim and final ability estimation. This approach is here reformulated, extended to incorporate a larger class of polytomous and dichotomous item response theory models, and investigated with simulation studies under different test-taking strategies.

Keywords: computerized adaptive testing; item response theory (IRT); dichotomous IRT models; polytomous IRT models; response revision; large sample property (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11336-019-09662-9

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