在存在非响应的情况下,对项目响应理论模型的剩余基调统计的评估
1University of Notre Dame, IN, USA.
Educational and psychological measurement
|December 29, 2025
概括
在项目响应理论 (IRT) 模型中缺少的数据会扭曲基于剩余的匹配统计数据. 删除方法会使准确性恶化,比模型的错误规格更多地膨胀合适指数,特别是缺失率增加.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 教育测量教育的测量
背景情况:
- 基于残余的适合统计对于评估物品响应理论 (IRT) 模型至关重要.
- 在经验研究中,项目非响应是常见的,但它对适应统计学的影响还没有得到充分研究.
- 当前的软件经常使用删除方法 (listwise/pairwise) 删除缺失的数据,可能会扭曲结果.
研究的目的:
- 系统地检查项目未响应对IRT模型中基于残余的合适统计数据的影响.
- 评估缺少数据的常见启发式处理的适当性,例如删除方法.
- 调查缺失数据的影响是否比模型错误规范更为重大.
主要方法:
- 进行了广泛的模拟研究.
- 该研究将观察到的项目统计数据与在各种缺失数据条件下模型隐含的概率进行了比较.
- 删除方法的性能在零和功率条件下进行了评估.
主要成果:
- 删除方法显著降低了IRT模型中适合性测试的准确性.
- 在使用删除方法时,适应指数在零和功率条件下都会被膨胀.
- 缺少数据对适应统计的负面影响比模型错误规范的负面影响更为明显,特别是随着缺失数量的增加.
结论:
- 在IRT中处理缺失数据的启发式删除方法不合适,并导致有偏见的匹配统计数据.
- 缺少的数据对合适性测试的有效性构成的威胁比模型的错误规格更大.
- 应用研究人员需要实用建议和替代方法,以适当考虑IRT分析中缺少的数据.
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