Journal
BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY
Volume 71, Issue 2, Pages 300-333Publisher
WILEY
DOI: 10.1111/bmsp.12119
Keywords
cognitive diagnosis; large sample theory; latent class model; maximum likelihood estimation; model misspecification; Q matrix
Ask authors/readers for more resources
The maximum likelihood classification rule is a standard method to classify examinee attribute profiles in cognitive diagnosis models (CDMs). Its asymptotic behaviour is well understood when the model is assumed to be correct, but has not been explored in the case of misspecified latent class models. This paper investigates the asymptotic behaviour of a two-stage maximum likelihood classifier under a misspecified CDM. The analysis is conducted in a general restricted latent class model framework addressing all types of CDMs. Sufficient conditions are proposed under which a consistent classification can be obtained by using a misspecified model. Discussions are also provided on the inconsistency of classification under certain model misspecification scenarios. Simulation studies and a real data application are conducted to illustrate these results. Our findings can provide some guidelines as to when a misspecified simple model or a general model can be used to provide a good classification result.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available