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

Performance of Fit Indices in Choosing Correct Cognitive Diagnostic Models and Q-Matrices 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Psychological Measurement SAGE

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

Applied Psychological Measurement , Volume 40 (6): 13 – Sep 1, 2016

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References (24)

Publisher
SAGE
Copyright
© The Author(s) 2016
ISSN
0146-6216
eISSN
1552-3497
DOI
10.1177/0146621616647954
Publisher site
See Article on Publisher Site

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.

Journal

Applied Psychological MeasurementSAGE

Published: Sep 1, 2016

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