ISSN 0439-755X
CN 11-1911/B

中国科学院心理研究所

Acta Psychologica Sinica ›› 2020, Vol. 52 ›› Issue (1): 93-106.

• Reports of Empirical Studies •

### A method of Q-matrix validation for polytomous response cognitive diagnosis model based on relative fit statistics

WANG Daxun1,GAO Xuliang2,CAI Yan1,TU Dongbo1()

1. 1 School of Psychology, Jiangxi Normal University, Nanchang 330022, China
2 School of Psychology, Guizhou Normal University, Guiyang 550000, China
• Received:2018-12-14 Online:2020-01-25 Published:2019-11-21
• Contact: Dongbo TU E-mail:tudongbo@aliyun.com

Abstract:

Cognitive diagnostic assessments (CDAs) can provide fine-grained diagnostic information about students' knowledge states, so as to help to teach in accordance with the students’ aptitude. The development of cognitive diagnosis model for polytomous response data expands the application scope of cognitive diagnostic assessment. As the basis of CDAs, Q-matrix has aroused more and more attention for the subjective tendency in Q-matrix construction that is typically performed by domain experts. Due to the subjective process of Q-matrix construction, there inevitably have some misspecifications in the Q-matrix, if left unchecked, can result in a serious negative impact on CDAs. To avoid the subjective tendency from experts and to improve the correctness of the Q-matrix, several objective Q-matrix validation methods have been proposed. Many Q-matrix validation methods have been proposed in dichotomous CDMs, however, the research of the Q-matrix validation method under polytomous CDMs is stalling lacking. To address this concern, several relative fit statistics (i.e., -2LL, AIC, BIC) were applied to the Q-matrix validation for polytomous cognitive diagnosis model in this research. The process of Q-matrix validation is as follows:
First, the reduced Q-matrix is represented by${{Q}_{r}}$, which represents a set of potential q-vectors and contains ${{2}^{K}}-1$ possible q-vectors when attributes are independent. When validating the q-vector of the first category of item j, all possible q-vectors in${{Q}_{r}}$can be used as the q-vector of the first category of item j, and the Q-matrix of remaining items remains intact. From this, the item parameters and the attribute patterns of students can be estimated, and the -2LL, AIC, and BIC can be calculated accordingly. The q-vector with the largest likelihood (or smallest AIC/BIC) is regarded as the q-vector of the first category of item j. The q-vector of the next category of the item j can also be obtained in the same way. The algorithm stops when the validated Q-matrix is same as the previous Q-matrix, or every item has been reached. In order to improve the efficiency of the method, a sequential search algorithm was proposed.
Several simulation studies were conducted to evaluate the effectiveness and practicality of these methods, and the performance of the methods in this paper was compared with the stepwise method ( Ma & de la Torre, 2019). Three experimental factors were considered in simulation studies, including sample size, Q-matrix error types and CDMs. The results show that (1) BIC method can be used for Q-matrix validation under polytomous response CDMs, and the performance of the BIC method is better than the stepwise method. (2) In general, the performance of the three methods from good to bad is the BIC method, AIC method, and -2LL method. (3) The performance of Q-matrix validation methods is affected by the sample size, and increasing the number of sample size can improve the accuracy of the Q-matrix validation.
In this study, Q-matrix validation methods for polytomous response CDMs were studied. It was found that the BIC method can be used for the Q-matrix validation under polytomous response CDMs. The method proposed in this paper can not only improve the accuracy of Q-matrix specification but also increase the model-data fit level. Besides, the data-based Q-matrix validation method can also reduce the workload of experts in Q-matrix construction and improve the classification accuracy of cognitive diagnosis.

CLC Number: