1. Performance of Fit Indices in Choosing Correct Cognitive Diagnostic Models and Q-Matrices.
- Author
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Lei, Pui-Wa and Li, Hongli
- Subjects
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COGNITION disorders diagnosis , *PSYCHOLOGICAL tests , *BAYESIAN analysis , *AKAIKE information criterion , *GOODNESS-of-fit tests , *PERFORMANCE standards - 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. 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. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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