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Computerized adaptive testing under nonparametric IRT models

Published online by Cambridge University Press:  01 January 2025

Xueli Xu  and
Jeff Douglas
Xueli Xu*
Affiliation:
Educational Testing Service
Jeff Douglas
Affiliation:
University of Illinois at Urbana-Champaign
*
Requests for reprints should be sent to Xueli Xu, Rosedale Road MS 02-T, Princeton, NJ 08541, USA.
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Abstract

Nonparametric item response models have been developed as alternatives to the relatively inflexible parametric item response models. An open question is whether it is possible and practical to administer computerized adaptive testing with nonparametric models. This paper explores the possibility of computerized adaptive testing when using nonparametric item response models. A central issue is that the derivatives of item characteristic Curves may not be estimated well, which eliminates the availability of the standard maximum Fisher information criterion. As alternatives, procedures based on Shannon entropy and Kullback–Leibler information are proposed. For a long test, these procedures, which do not require the derivatives of the item characteristic eurves, become equivalent to the maximum Fisher information criterion. A simulation study is conducted to study the behavior of these two procedures, compared with random item selection. The study shows that the procedures based on Shannon entropy and Kullback–Leibler information perform similarly in terms of root mean square error, and perform much better than random item selection. The study also shows that item exposure rates need to be addressed for these methods to be practical.

Keywords

Shannon entropyKullback–Leibler informationnonparametric item response modelsitem response theory

Information

Type
Original Paper
Information
Psychometrika , Volume 71 , Issue 1 , March 2006 , pp. 121 - 137
Copyright
Copyright © 2006 The Psychometric Society

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Footnotes

The authors would like to thank Hua Chang for his help in conducting this research.

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