Improved Performance of Model Fit Indices with Small Sample Sizes in Cognitive Diagnostic Models

Selecting an appropriate cognitive diagnostic model insert ignore into journalissuearticles values(CDM); for data analysis is always challenging. Studies have explored several model fit indices for CDMs. The common results of these studies indicate that Q-matrix misspecifications lead to poor performance of the model fit indices in the context of CDMs. Thus, this study explored whether model fit indices improve performance with a modified Q-matrix. The average class size has reduced to 23 students in Taiwan because of the low birth rate; therefore, the study sought the effect of sample size on the performance of model fit indices. The results showed that Akaike’s information criterion insert ignore into journalissuearticles values(AIC); was an excellent model fit index in small samples. Model fit indices with the modified Q-matrix presented superior performance.