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Diagnostic Classification Models for Testlets: Methods and Theory

Published online by Cambridge University Press:  01 January 2025

Xin Xu
Affiliation:
Beijing Normal University
Guanhua Fang
Affiliation:
Fudan University
Jinxin Guo
Affiliation:
Minzu University of China
Zhiliang Ying
Affiliation:
Columbia University
Susu Zhang*
Affiliation:
University of Illinois Urbana-Champaign
*
Correspondence should be made to Susu Zhang, University of Illinois Urbana-Champaign, Champaign, USA. Email: [email protected]

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.

Type
Theory & Methods
Copyright
Copyright © 2024 The Author(s), under exclusive licence to The Psychometric Society

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