On the Identifiability of Diagnostic Classification Models
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.
Cite
@article{arxiv.1706.01240,
title = {On the Identifiability of Diagnostic Classification Models},
author = {Guanhua Fang and Jingchen Liu and Zhiliang Ying},
journal= {arXiv preprint arXiv:1706.01240},
year = {2025}
}