Cognitive diagnostic models (CDMs) are of growing interest in educational research because of the models’ ability to provide diagnostic information regarding examinees’ strengths and weaknesses suited to a variety of content areas. An important step to ensure appropriate uses and interpretations from CDMs is to understand the impact of differential item functioning (DIF). While methods of detecting DIF in CDMs have been identified, there is a limited understanding of the extent to which DIF affects classification accuracy. This simulation study provides a reference to practitioners to understand how different magnitudes and types of DIF interact with CDM item types and group distributions and sample sizes to influence attribute- and profile-level classification accuracy. The results suggest that attribute-level classification accuracy is robust to DIF of large magnitudes in most conditions, while profile-level classification accuracy is negatively influenced by the inclusion of DIF. Conditions of unequal group distributions and DIF located on simple structure items had the greatest effect in decreasing classification accuracy. The article closes by considering implications of the results and future directions.

中文翻译：

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在认知诊断模型中检查差异项功能对分类准确性的影响。

认知诊断模型（CDM）在教育研究中的兴趣日益浓厚，因为该模型能够提供有关考生适合各种内容领域的优缺点的诊断信息。确保CDM正确使用和解释的重要步骤是了解差异项目功能（DIF）的影响。虽然已经确定了在CDM中检测DIF的方法，但对DIF在多大程度上影响分类准确性的理解仍然有限。该模拟研究为从业人员提供参考，以了解不同大小和类型的DIF如何与CDM项目类型，组分布和样本大小相互作用，从而影响属性和配置文件级别的分类准确性。结果表明，在大多数情况下，属性级别的分类精度对大幅度的DIF具有鲁棒性，而配置文件级别的分类精度则受到DIF的负面影响。在简单的结构项目上存在不相等的组分布和DIF的条件对降低分类精度的影响最大。最后，本文将考虑结果的含义和未来的方向。