Performance of Fit Indices in Choosing Correct Cognitive Diagnostic Models and Q-Matrices.

ABSTRACT: In applications of cognitive diagnostic models (CDMs), practitioners usually face the difficulty of choosing appropriate CDMs and building accurate Q-matrices. However, functions of model-fit indices that are supposed to inform model and Q-matrix choices are not well understood. This study examines the performance of several promising model-fit indices in selecting model and Q-matrix under different sample size conditions. Relative performance between Akaike information criterion and Bayesian information criterion in model and Q-matrix selection appears to depend on the complexity of data generating models, Q-matrices, and sample sizes. Among the absolute fit indices, MX2 is least sensitive to sample size under correct model and Q-matrix specifications, and performs the best in power. Samp

SUBMITTER: Lei PW 

PROVIDER: S-EPMC5978496 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature