ISSN 0439-755X
CN 11-1911/B

Acta Psychologica Sinica ›› 2023, Vol. 55 ›› Issue (1): 142-158.doi: 10.3724/SP.J.1041.2023.00142

• Reports of Empirical Studies • Previous Articles    

An empirical Q-matrix validation method using complete information matrix in cognitive diagnostic models

LIU Yanlou1(), WU Qiongqiong2   

  1. 1Academy of Big Data for Education; Qufu Normal University, Jining 273165, China
    2School of Psychology, Qufu Normal University, Jining 273165, China
  • Published:2023-01-25 Online:2022-10-18
  • Contact: LIU Yanlou


A Q-matrix, which defines the relations between latent attributes and items, is a central building block of the cognitive diagnostic models (CDMs). In practice, a Q-matrix is usually specified subjectively by domain experts, which might contain some misspecifications. The misspecified Q-matrix could cause several serious problems, such as inaccurate model parameters and erroneous attribute profile classifications. Several Q-matrix validation methods have been developed in the literature, such as the G-DINA discrimination index (GDI), Wald test based on an incomplete information matrix (Wald-IC), and Hull methods. Although these methods have shown promising results on Q-matrix recovery rate (QRR) and true positive rate (TPR), a common drawback of these methods is that they obtain poor results on true negative rate (TNR). It is important to note that the worse performance of the Wald-IC method on TNR might be caused by the incorrect computation of the information matrix.

A new Q-matrix validation method is proposed in this paper that constructs a Wald test with a complete empirical cross-product information matrix (XPD). A simulation study was conducted to evaluate the performance of the Wald-XPD method and compare it with GDI, Wald-IC, and Hull methods. Five factors that may influence the performance of Q-matrix validation were manipulated. Attribute patterns were generated following either a uniform distribution or a higher-order distribution. The misspecification rate was set to two levels: QM = 0.15 and QM= 0.3. Two sample sizes were manipulated: 500 and 1000. The three levels of item quality (IQ) were defined as high IQ, Pj(0) ~ U(0, 0.2) and Pj(1) ~ U(0.8, 1); medium IQ, Pj(0) ~ U(0.1, 0.3) and Pj(1) ~ U(0.7, 0.9); and low IQ, Pj(0) ~ U(0.2, 0.4) and Pj(1) ~ U(0.6, 0.8). The number of attributes was fixed at K = 4. Two ratios of the number of items to attribute were considered in the study: J = 16[(K= 4) × (JK = 4)] and J = 32[(K= 4) × (JK = 8)].

The QRR, TPR, and TNR of the simulation results of the HullP、Wald-IC and Wald-XPD methods are displayed in Figures 1 to 3.

(1) The Wald-XPD method always provided the best results or was close to the best-performing method across the different factor levels, especially in the terms of the TNR, as illustrated in Figure 3. The HullP and Wald-IC methods produced larger values of QRR and TPR but smaller values of TNR, as shown in Figures 1 and 2. A similar pattern was observed between HullP and HullR, with HullP being better than HullR. Among the Q-matrix validation methods considered in this study, the GDI method was the worst performer.

(2) The results from the comparison of the HullP, Wald-IC, and Wald-XPD methods suggested that the Wald-XPD method is more preferred for Q-matrix validation. Even though the HullP and Wald-IC methods could provide higher TPR values when the conditions were particularly unfavorable (e.g., low item quality, short test length, and low sample size), they obtain very low TNR values. The practical application of the Wald-XPD method was illustrated using real data.

In conclusion, the Wald-XPD method has excellent power to detect and correct misspecified q-entry. In addition, it is a generic method that can serve as an important complement to domain experts’ judgement, which could reduce their workload.

Key words: cognitive diagnostic models, Q-matrix, XPD information matrix, Wald test