Comparison of Cognitive Diagnosis Models under Changing Conditions: DINA, RDINA, HODINA and HORDINA

Abstract

The application of CDMs to fraction subtraction data revealed problems on the classification of examinees, latent class sizes, and the use of higher-order models. Additionally, selecting the most appropriate model assumes critical importance if there are several appropriate models available for the data. In the present study, DINA–RDINA and HODINA–HORDINA models were compared under changing conditions (i.e., number of attributes, g and s item parameter values, and number of items) with simulated and real data. The results show that for conditions where the g–s parameter values and the number of attributes were low (0.1 and 3, respectively), the reparameterized models generated values that were virtually identical to those obtained using DINA models. However, when the g–s parameter values and the number of attributes were increased (0.5 and 5, respectively), the parameter estimations obtained from the models, latent class estimates, AIC, and BIC show differences through the values from the models.