TY - JOUR
T1 - Performance of Fit Indices in Choosing Correct Cognitive Diagnostic Models and Q-Matrices
AU - Lei, Pui Wa
AU - Li, Hongli
N1 - Publisher Copyright:
© 2016, © The Author(s) 2016.
PY - 2016/9/1
Y1 - 2016/9/1
N2 - 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.
AB - 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.
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U2 - 10.1177/0146621616647954
DO - 10.1177/0146621616647954
M3 - Article
AN - SCOPUS:84982931591
SN - 0146-6216
VL - 40
SP - 405
EP - 417
JO - Applied Psychological Measurement
JF - Applied Psychological Measurement
IS - 6
ER -