Abstract
In this study, item and model data fit indices, calculated by DINA and G-DINA Models using the same sample and Q matrix, are analyzed. Fit indices for these two models from Cognitive Diagnostic Models are analyzed using 2LL, AIC and BIC statistics. Item fit indices are analyzed using residual correlations and probabilities. Analysis results showed G-DINA model had better fit results than DINA model. DINA model could give rather better results to estimate student profile in tests where higher level and progressive behaviors are used together. On the other hand, G-DINA model weights required attributes for an item when estimating student profile. Therefore in items requiring more than one attributes, contributions of attributes to probability that a student answers the item correctly are not equal. This provides an important advantage to testers to evaluate multiple choice items in assessing complex and prerequisite forming patterns.
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