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arXiv · 1706.01240

On the Identifiability of Diagnostic Classification Models

Guanhua Fang·Jingchen Liu·Zhiliang Ying
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Abstract

This paper establishes fundamental results for statistical inference of diagnostic classification models (DCM). The results are developed at a high level of generality, applicable to essentially all diagnostic classification models. In particular, we establish identifiability results of various modeling parameters, notably item response probabilities, attribute distribution, and Q-matrix-induced partial information structure. Consistent estimators are constructed. Simulation results show that these estimators perform well under various modeling settings. We also use a real example to illustrate the new method. The results are stated under the setting of general latent class models. For DCM with a specific parameterization, the conditions may be adapted accordingly.

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Guanhua Fang, Jingchen Liu, Zhiliang Ying. 2017-06-05. On the Identifiability of Diagnostic Classification Models. https://doi.org/10.1007/s11336-018-09658-x

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