Applying the M2 Statistic to Evaluate the Fit of Diagnostic Classification Models in the Presence of Attribute Hierarchies

• Main Authors: Fu Chen, Yanlou Liu, Tao Xin, Ying Cui
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2018-10-01
• Series: Frontiers in Psychology
• Subjects: diagnostic classification models; attribute hierarchies; absolute fit test; limited-information test statistics; goodness-of-fit
• Online Access: https://www.frontiersin.org/article/10.3389/fpsyg.2018.01875/full

The performance of the limited-information statistic M2 for diagnostic classification models (DCMs) is under-investigated in the current literature. Specifically, the investigations of M2 for specific DCMs rather than general modeling frameworks are needed. This article aims to demonstrate the usefulness of M2 in hierarchical diagnostic classification models (HDCMs). The performance of M2 in evaluating the fit of HDCMs was investigated in the presence of four types of attribute hierarchies. Two simulation studies were conducted to examine Type I error rates and statistical power of M2 under different simulation conditions, respectively. The findings suggest acceptable Type I error rates control of M2 as well as high statistical power under the conditions of a Q-matrix misspecification and the DINA model misspecification. The data of Examination for the Certificate of Proficiency in English (ECPE) were used to empirically illustrate the suitability of M2 in practice.