ISSN 1671-3710
CN 11-4766/R

Advances in Psychological Science ›› 2021, Vol. 29 ›› Issue (12): 2272-2280.doi: 10.3724/SP.J.1042.2021.02272

• Research Method • Previous Articles    

Q-matrix estimation (validation) methods for cognitive diagnosis

LI Jia, MAO Xiuzhen(), ZHANG Xueqin   

  1. Institute of Educational Sichuan Normal University, Chengdu 610066, China
  • Received:2021-04-02 Published:2021-10-26
  • Contact: MAO Xiuzhen


The Q-matrix, which represents important item characteristics by mapping attributes to items has been proved to be the core factor affecting the accuracy of cognitive diagnostic classification. It is of great value to study the methods of Q-matrix estimation (validation). First, the existing methods of Q-matrix estimation and validation are classified into 1) parameterized methods in the CDM perspective, including item differentiation, model-data fit index and parameter estimation; and 2) non-parametric methods in the statistical perspective, including the distance between observed and expected response vector, abnormal responses index and factor analysis. Then, these methods are introduced in terms of differences and relations, characteristics and performance. The advantages and disadvantages of each method are commented. At last, several future research directions are proposed. It is necessary to compare the Q-matrix estimation (validation) methods systematically under complex test conditions. It is also of vital importance to propose Q-matrix estimation (validation) methods by combining multiple thoughts and ways based on the calibration of knowledge state and parameter estimation error. It is meaningful to further study the Q-matrix estimation (validation) methods for polytomous scoring items, mixed test models, polytomous scoring attributes, unknown number of attributes and even continuous Q-matrix.

Key words: cognitive diagnosis models, Q-matrix, Q-matrix estimation (validation) methods, model-data fit, parameter estimation

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