Improving robustness in Q-Matrix validation using an iterative and dynamic procedure
EntityUAM. Departamento de Psicología Social y Metodología
10.1177/0146621620909904Applied Psychological Measurement 44.6 (2020): 431-446
ISSN1552-3497 (online); 0146-6216 (print)
Funded byThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was partially supported by Ministerio de Ciencia, Innovación y Universidades, Spain (Grant No. PSI2017-85022-P) and Cátedra de Modelos y Aplicaciones Psicométricas (Instituto de Ingeniería del Conocimiento and Universidad Autónoma de Madrid)
ProjectGobierno de España. PSI2017-85022-P
SubjectsCDM; G-DINA; GDI; Q-matrix; validation; Psicología
Rights© The Author(s) 2020
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-matrix specification. To address this, the present study investigates the iterative application of the GDI method, where only one item is modified at each step of the iterative procedure, and the required cutoff is updated considering the new parameter estimates. A simulation study was conducted to test the performance of the new procedure. Results showed that the performance of the GDI method improved when the application was iterative at the item level and an appropriate cutoff point was used. This was most notable when the original Q-matrix misspecification rate was high, where the proposed procedure performed better 96.5% of the times. The results are illustrated using Tatsuoka’s fraction-subtraction data set.
Google Scholar:Najera Álvarez, Pablo - Sorrel Luján, Miguel Ángel - de la Torre, Jimmy - Abad García, Francisco José
This item appears in the following Collection(s)
Showing items related by title, author, creator and subject.