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Generating Adaptive and Non-Adaptive Test Interfaces for Multidimensional Item Response Theory Applications

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  • Chalmers, R. Philip

Abstract

Computerized adaptive testing (CAT) is a powerful technique to help improve measurement precision and reduce the total number of items required in educational, psychological, and medical tests. In CATs, tailored test forms are progressively constructed by capitalizing on information available from responses to previous items. CAT applications primarily have relied on unidimensional item response theory (IRT) to help select which items should be administered during the session. However, multidimensional CATs may be constructed to improve measurement precision and further reduce the number of items required to measure multiple traits simultaneously. A small selection of CAT simulation packages exist for the R environment; namely, catR (Magis and Raîche 2012), catIrt (Nydick 2014), and MAT (Choi and King 2014). However, the ability to generate graphical user interfaces for administering CATs in realtime has not been implemented in R to date, support for multidimensional CATs have been limited to the multidimensional three-parameter logistic model, and CAT designs were required to contain IRT models from the same modeling family. This article describes a new R package for implementing unidimensional and multidimensional CATs using a wide variety of IRT models, which can be unique for each respective test item, and demonstrates how graphical user interfaces and Monte Carlo simulation designs can be constructed with the mirtCAT package.

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  • Chalmers, R. Philip, 2016. "Generating Adaptive and Non-Adaptive Test Interfaces for Multidimensional Item Response Theory Applications," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i05).
    • Handle: RePEc:jss:jstsof:v:071:i05
    DOI: http://hdl.handle.net/10.18637/jss.v071.i05
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    References listed on IDEAS

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    1. David Thissen, 1982. "Marginal maximum likelihood estimation for the one-parameter logistic model," Psychometrika, Springer;The Psychometric Society, vol. 47(2), pages 175-186, June.
    2. Joris Mulder & Wim Linden, 2009. "Multidimensional Adaptive Testing with Optimal Design Criteria for Item Selection," Psychometrika, Springer;The Psychometric Society, vol. 74(2), pages 273-296, June.
    3. R. Bock & Murray Aitkin, 1981. "Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm," Psychometrika, Springer;The Psychometric Society, vol. 46(4), pages 443-459, December.
    4. Youngsuk Suh & Daniel Bolt, 2010. "Nested Logit Models for Multiple-Choice Item Response Data," Psychometrika, Springer;The Psychometric Society, vol. 75(3), pages 454-473, September.
    5. Magis, David & Raîche, Gilles, 2012. "Random Generation of Response Patterns under Computerized Adaptive Testing with the R Package catR," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i08).
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