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Diagnostic Classification Models for Testlets: Methods and Theory

Author

Listed:
  • Xin Xu

    (Beijing Normal University)

  • Guanhua Fang

    (Fudan University)

  • Jinxin Guo

    (Minzu University of China)

  • Zhiliang Ying

    (Columbia University)

  • Susu Zhang

    (University of Illinois Urbana-Champaign)

Abstract

Diagnostic classification models (DCMs) have seen wide applications in educational and psychological measurement, especially in formative assessment. DCMs in the presence of testlets have been studied in recent literature. A key ingredient in the statistical modeling and analysis of testlet-based DCMs is the superposition of two latent structures, the attribute profile and the testlet effect. This paper extends the standard testlet DINA (T-DINA) model to accommodate the potential correlation between the two latent structures. Model identifiability is studied and a set of sufficient conditions are proposed. As a byproduct, the identifiability of the standard T-DINA is also established. The proposed model is applied to a dataset from the 2015 Programme for International Student Assessment. Comparisons are made with DINA and T-DINA, showing that there is substantial improvement in terms of the goodness of fit. Simulations are conducted to assess the performance of the new method under various settings.

Suggested Citation

  • Xin Xu & Guanhua Fang & Jinxin Guo & Zhiliang Ying & Susu Zhang, 2024. "Diagnostic Classification Models for Testlets: Methods and Theory," Psychometrika, Springer;The Psychometric Society, vol. 89(3), pages 851-876, September.
    • Handle: RePEc:spr:psycho:v:89:y:2024:i:3:d:10.1007_s11336-024-09962-9
    DOI: 10.1007/s11336-024-09962-9
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