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

Pui Wa Lei, Hongli Li

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


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. Sample size is found to be the most influential factor on model-fit index values. Consequences of selecting inaccurate model and Q-matrix in classification accuracy of attribute mastery are also evaluated.

Original languageEnglish (US)
Pages (from-to)405-417
Number of pages13
JournalApplied Psychological Measurement
Issue number6
StatePublished - Sep 1 2016

All Science Journal Classification (ASJC) codes

  • Social Sciences (miscellaneous)
  • Psychology (miscellaneous)


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