Improving Robustness in Q-Matrix Validation Using an Iterative and Dynamic Procedure.

ABSTRACT: In the context of cognitive diagnosis models (CDMs), a Q-matrix reflects the correspondence between attributes and items. The Q-matrix construction process is typically subjective in nature, which may lead to misspecifications. All this can negatively affect the attribute classification accuracy. In response, several methods of empirical Q-matrix validation have been developed. The general discrimination index (GDI) method has some relevant advantages such as the possibility of being applied to several CDMs. However, the estimation of the GDI relies on the estimation of the latent group sizes and success probabilities, which is made with the original (possibly misspecified) Q-matrix. This can be a problem, especially in those situations in which there is a great uncertainty about the Q-mat

SUBMITTER: Najera P 

PROVIDER: S-EPMC7383688 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature