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Optimizing Diagnostic Classification Models Application Considering Real-Life Constraints

Author

Listed:
  • Kun Su

    (Sun Yat-Sen University)

  • Robert A. Henson

    (University of North Carolina at Greensboro)

Abstract

This article provides a process to carefully evaluate the suitability of a content domain for which diagnostic classification models (DCMs) could be applicable and then optimized steps for constructing a test blueprint for applying DCMs and a real-life example illustrating this process. The content domains were carefully evaluated using a set of defined criteria, which are purposely defined to improve the success rate of DCM implementation. Given the domain, the Q-matrix is determined by a simulation-based approach using correct classification rates as criteria. Finally, a physics test on the final Q-matrix was developed, administered, and analyzed by the author and the subject-matter experts (SMEs).

Suggested Citation

  • Kun Su & Robert A. Henson, 2023. "Optimizing Diagnostic Classification Models Application Considering Real-Life Constraints," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 750-772, December.
    • Handle: RePEc:sae:jedbes:v:48:y:2023:i:6:p:750-772
    DOI: 10.3102/10769986231159137
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    References listed on IDEAS

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    1. Jimmy Torre & Jeffrey Douglas, 2004. "Higher-order latent trait models for cognitive diagnosis," Psychometrika, Springer;The Psychometric Society, vol. 69(3), pages 333-353, September.
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    Cited by:

    1. Steven Andrew Culpepper & Gongjun Xu, 2023. "Introduction to JEBS Special Issue on Diagnostic Statistical Models," Journal of Educational and Behavioral Statistics, , vol. 48(6), pages 687-689, December.

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