Diagnostic Classification Models for Testlets: Methods and Theory

Xin Xu, Guanhua Fang, Jinxin Guo, Zhiliang Ying and Susu Zhang ()
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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

Psychometrika, 2024, vol. 89, issue 3, No 6, 876 pages

Abstract: 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.

Keywords: diagnostic classification model; testlet DINA; identifiability; PISA; Q-matrix; interaction; hypothesis testing; model selection (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11336-024-09962-9

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