Cai Yan, Tu Dongbo, Ding Shuliang
School of Psychology, Jiangxi Normal University, Nanchang, China.
School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, China.
Front Psychol. 2018 Aug 8;9:1413. doi: 10.3389/fpsyg.2018.01413. eCollection 2018.
The design of test Q matrix can directly influence the classification accuracy of a cognitive diagnostic assessment. In this paper, we focus on Q matrix design when attribute hierarchies are known prior to test development. A complete Q matrix design is proposed and theorems are presented to demonstrate that it is a necessary and sufficient condition to guarantee the identifiability of ideal response patterns. A simulation study is also conducted to detect the effects of the proposed design on a family of conjunctive diagnostic models. The results revealed that the proposed Q matrix design is the key condition for guaranteeing classification accuracy. When only one type of item pattern in R matrix is missing from the associated test Q matrix, the related attribute-wise agreement rate will decrease dramatically. When the entire R matrix is missing, both the pattern-wise and attribute-wise agreement rates will decrease sharply. This indicates that the proposed procedures for complete Q matrix design with attribute hierarchies can serve as guidelines for test blueprint development prior to item writing in a cognitive diagnostic assessment.
测试Q矩阵的设计会直接影响认知诊断评估的分类准确性。在本文中,我们聚焦于在测试开发之前属性层次已知时的Q矩阵设计。提出了一种完整的Q矩阵设计,并给出定理以证明它是保证理想反应模式可识别性的充要条件。还进行了一项模拟研究,以检测所提出的设计对一类合取诊断模型的影响。结果表明,所提出的Q矩阵设计是保证分类准确性的关键条件。当相关测试Q矩阵中仅缺少R矩阵中的一种项目模式类型时,相关的按属性一致性率将大幅下降。当整个R矩阵缺失时,按模式和按属性的一致性率都会急剧下降。这表明所提出的具有属性层次的完整Q矩阵设计程序可作为认知诊断评估中项目编写前测试蓝图开发的指导方针。