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A Longitudinal Higher-Order Diagnostic Classification Model

Author

Listed:
  • Peida Zhan

    (Zhejiang Normal University)

  • Hong Jiao

    (University of Maryland)

  • Dandan Liao

    (American Institutes for Research)

  • Feiming Li

    (Zhejiang Normal University)

Abstract

Providing diagnostic feedback about growth is crucial to formative decisions such as targeted remedial instructions or interventions. This article proposed a longitudinal higher-order diagnostic classification modeling approach for measuring growth. The new modeling approach is able to provide quantitative values of overall and individual growth by constructing a multidimensional higher-order latent structure to take into account the correlations among multiple latent attributes that are examined across different occasions. In addition, potential local item dependence among anchor (or repeated) items can be taken into account. Model parameter estimation is explored in a simulation study. An empirical example is analyzed to illustrate the applications and advantages of the proposed modeling approach.

Suggested Citation

  • Peida Zhan & Hong Jiao & Dandan Liao & Feiming Li, 2019. "A Longitudinal Higher-Order Diagnostic Classification Model," Journal of Educational and Behavioral Statistics, , vol. 44(3), pages 251-281, June.
    • Handle: RePEc:sae:jedbes:v:44:y:2019:i:3:p:251-281
    DOI: 10.3102/1076998619827593
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    References listed on IDEAS

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    Cited by:

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    2. Hiroshi Tamano & Daichi Mochihashi, 2023. "Dynamical Non-compensatory Multidimensional IRT Model Using Variational Approximation," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 487-526, June.
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