Dai Shenghai, Svetina Dubravka, Chen Cong
Washington State University, Pullman, USA.
Indiana University, Bloomington, USA.
Appl Psychol Meas. 2018 Nov;42(8):660-676. doi: 10.1177/0146621618762742. Epub 2018 Mar 26.
Missing data can be a serious issue for practitioners and researchers who are tasked with Q-matrix validation analysis in implementation of cognitive diagnostic models. The article investigates the impact of missing responses, and four common approaches (treat as incorrect, logistic regression, listwise deletion, and expectation-maximization [EM] imputation) for dealing with them, on the performance of two major Q-matrix validation methods (the EM-based δ-method and the nonparametric Q-matrix refinement method) across multiple factors. Results of the simulation study show that both validation methods perform better when missing responses are imputed using EM imputation or logistic regression instead of being treated as incorrect and using listwise deletion. The nonparametric Q-matrix validation method outperforms the EM-based δ-method in most conditions. Higher missing rates yield poorer performance of both methods. Number of attributes and items have an impact on performance of both methods as well. Results of a real data example are also discussed in the study.
对于负责在认知诊断模型实施中进行Q矩阵验证分析的从业者和研究人员而言，数据缺失可能是一个严重的问题。本文研究了缺失回答的影响，以及处理这些缺失回答的四种常见方法（视为错误回答、逻辑回归、列表删除和期望最大化[EM]插补）对两种主要Q矩阵验证方法（基于EM的δ方法和非参数Q矩阵细化方法）在多个因素上的性能的影响。模拟研究结果表明，当使用EM插补或逻辑回归对缺失回答进行插补，而不是将其视为错误回答并使用列表删除时，两种验证方法的性能都更好。在大多数情况下，非参数Q矩阵验证方法的表现优于基于EM的δ方法。更高的缺失率会导致两种方法的性能更差。属性和项目的数量也会对两种方法的性能产生影响。该研究还讨论了一个真实数据示例的结果。
