Cognitive Diagnosis Modeling Incorporating Item-Level Missing Data Mechanism.

ABSTRACT: The aim of cognitive diagnosis is to classify respondents' mastery status of latent attributes from their responses on multiple items. Since respondents may answer some but not all items, item-level missing data often occur. Even if the primary interest is to provide diagnostic classification of respondents, misspecification of missing data mechanism may lead to biased conclusions. This paper proposes a joint cognitive diagnosis modeling of item responses and item-level missing data mechanism. A Bayesian Markov chain Monte Carlo (MCMC) method is developed for model parameter estimation. Our simulation studies examine the parameter recovery under different missing data mechanisms. The parameters could be recovered well with correct use of missing data mechanism for model fit, and missing th

SUBMITTER: Shan N 

PROVIDER: S-EPMC7733994 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature