Cognitive Diagnosis Modeling Incorporating Item-Level Missing Data Mechanism

Na Shan; Xiaofei Wang

doi:10.3389/fpsyg.2020.564707

Cognitive Diagnosis Modeling Incorporating Item-Level Missing Data Mechanism

Front Psychol. 2020 Nov 30:11:564707. doi: 10.3389/fpsyg.2020.564707. eCollection 2020.

Authors

Na Shan^{1

2}, Xiaofei Wang^{2

3}

Affiliations

¹ School of Psychology, Northeast Normal University, Changchun, China.
² Key Laboratory of Applied Statistics of the Ministry of Education, Northeast Normal University, Changchun, China.
³ School of Mathematics and Statistics, Northeast Normal University, Changchun, China.

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 that is not at random is less sensitive to incorrect use. The Program for International Student Assessment (PISA) 2015 computer-based mathematics data are applied to demonstrate the practical value of the proposed method.

Keywords: cognitive diagnosis; cognitive diagnosis model; item-level; missing data; missing data mechanism.