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Conditional Independence and Dimensionality of Cognitive Diagnostic Models: a Test for Model Fit

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  • Youn Seon Lim

    (Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University)

  • Fritz Drasgow

    (University of Illinois at Urbana-Champaign)

Abstract

Nonparametric cognitive diagnosis methods are useful in cognitive diagnosis modeling for calibration efficiency, especially when sample size is small or large, or the latent attributes are more complex. This article proposes the Mantel-Haenszel chi-squared statistic as an index for detecting the misspecification of latent attributes as well as testlet effects in nonparametric cognitive diagnosis methods. The proposed theoretical considerations are augmented by simulation studies conducted to assess the performance of the Mantel-Haenszel statistic under various conditions within the nonparametric diagnosis framework, with a special focus on situations were the set of latent abilities assumed to underlie the data was underspecified.

Suggested Citation

  • Youn Seon Lim & Fritz Drasgow, 2019. "Conditional Independence and Dimensionality of Cognitive Diagnostic Models: a Test for Model Fit," Journal of Classification, Springer;The Classification Society, vol. 36(2), pages 295-305, July.
    • Handle: RePEc:spr:jclass:v:36:y:2019:i:2:d:10.1007_s00357-018-9287-5
    DOI: 10.1007/s00357-018-9287-5
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    References listed on IDEAS

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    6. Chen, Yunxiao & Liu, Jingchen & Xu, Gongjun & Ying, Zhiliang, 2015. "Statistical analysis of Q-matrix based diagnostic classification models," LSE Research Online Documents on Economics 103183, London School of Economics and Political Science, LSE Library.
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