A Note on Comparing Examinee Classification Methods for Cognitive Diagnosis Models

Abstract

Cognitive diagnosis models have received much attention in the recent psychometric literature because of their potential to provide examinees with information regarding multiple fine-grained discretely defined skills, or attributes. This article discusses the issue of methods of examinee classification for cognitive diagnosis models, which are special cases of restricted latent class models. Specifically, the maximum likelihood estimation and maximum a posteriori classification methods are compared with the expected a posteriori method. A simulation study using the Deterministic Input, Noisy-And model is used to assess the classification accuracy of the methods using various criteria.