使用未知组和项目进行DIF分析
Gabriel Wallin1, Yunxiao Chen2, Irini Moustaki2
1Department of Mathematics and Statistics, Lancaster University.
Psychometrika
|February 25, 2026
概括
本研究引入了一个新的统计框架,用于差异性项目功能 (DIF) 分析,当分组和项目信息未知时. 该方法使用隐性类和L1规范化来识别DIF项目并估计群体差异,提高评估的公平性.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 教育测量的教育测量.
背景情况:
- 确保调查和测试的公平性至关重要.
- 差异性项目功能 (DIF) 分析评估项目级测量不变性.
- 传统的DIF方法需要已知的比较组和点,这些通常是不可用的.
研究的目的:
- 为DIF分析提出一个一般的统计框架,当两个比较组和项目是未知的.
- 开发一种方法,同时识别潜在子组和DIF项目.
- 为解决拟议模型提供一个计算效率高的算法.
主要方法:
- 一个新的统计框架,通过隐藏类来建模未知组.
- 引入项目特定的DIF参数.
- 一个L1规范化的估计器,同时识别隐性类和DIF项目.
- 一个计算效率高的预期-最大化 (EM) 算法用于优化.
主要成果:
- 拟议的框架有效地处理DIF分析,而无需事先了解集团或项目.
- 模拟研究证明了该方法的性能.
- 该方法成功地应用于现实世界的教育测试数据.
结论:
- 开发的统计框架为在具有挑战性的场景中进行DIF分析提供了强大的解决方案.
- 这种方法增强了教育和调查工具中测量不变性和公平性的评估.
- 这些发现有助于推进用于检测项目偏差的心理测量方法.
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