摘要：Q-matrix validation is one of the most vital parts in cognitive diagnosis, as the misspecification of Q-matrix may seriously influence the model fit and lead to incorrect classifications of examinees. In this paper, we propose a symmetrised Kullback–Leibler divergence- (SKLD-) based method to validate misspecified Q-matrix with a combination of K-means clustering. Three simulation studies are conducted to evaluate the sensitivity and specificity of the proposed method compared with that based on log odds ratio (LOR) and item discrimination index (IDI). The results show that the SKLD-based method could efficiently identify and validate misspecified elements in Q-matrix, and at the same time retain those correct ones. What’s more, two real data sets are employed to further illustrate the performance of SKLD-based method..
关键词：Q;matrix; misspecification; DINA model; SKLD; IDI; LOR; K;means clustering
