As a core component of most cognitive diagnosis models, the Q‐matrix, or item and attribute association matrix, is typically developed by domain experts, and tends to be subjective. It is critical to validate the Q‐matrix empirically because a misspecified Q‐matrix could result in erroneous attribute estimation. Most existing Q‐matrix validation procedures are developed for dichotomous responses. However, in this paper, we propose a method to empirically detect and correct the misspecifications in the Q‐matrix for graded response data based on the sequential generalized deterministic inputs, noisy ‘and’ gate (G‐DINA) model. The proposed Q‐matrix validation procedure is implemented in a stepwise manner based on the Wald test and an effect size measure. The feasibility of the proposed method is examined using simulation studies. Also, a set of data from the Trends in International Mathematics and Science Study (TIMSS) 2011 mathematics assessment is analysed for illustration.

中文翻译：

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序列广义DINA模型的经验Q矩阵验证方法。

作为大多数认知诊断模型的核心组件，Q矩阵（即项目和属性关联矩阵）通常由领域专家开发，并且往往是主观的。凭经验验证Q矩阵至关重要，因为错误指定的Q矩阵可能会导致错误的属性估计。大多数现有的Q矩阵验证程序都是针对二分响应而开发的。但是，在本文中，我们提出了一种基于顺序广义确定性输入，噪声“和”门（G-DINA）模型的经验检测和校正Q矩阵中分级响应数据的错误指定的方法。拟议的Q矩阵验证程序是基于Wald检验和效应大小度量以逐步方式实施的。通过仿真研究检验了该方法的可行性。也，