使用未知组和项目进行DIF分析
Gabriel Wallin1, Yunxiao Chen2, Irini Moustaki3
1Department of Mathematics and Statistics, Lancaster University, Umeå, Sweden.
Psychometrika
|February 22, 2024
概括
本研究引入了一种新的统计方法,用于在未知子组或项时进行差异性项目功能 (DIF) 分析. 该方法使用隐性类和L1规范化来识别测试和调查中的公平性问题.
科学领域:
- 心理测量 心理测量 心理测量
- 教育测量教育的测量
- 统计建模 统计建模
背景情况:
- 确保调查问卷和教育测试的公平性至关重要.
- 差异性项目功能 (DIF) 分析通过检测子组响应差异来评估项目级测量不变性.
- 传统的DIF方法需要预定义的参考/焦点组和点,这些并不总是可用.
研究的目的:
- 提出一个新的统计框架,用于DIF分析,当两个比较组和项目是未知的.
- 开发一种方法,在没有事先信息的情况下,同时识别潜在子组和DIF项目.
- 提供一种强有力的方法,以提高评估中的公平性.
主要方法:
- 提出了一个一般的统计框架,通过隐藏类来建模未知组.
- 引入了项目特定的DIF参数,以捕捉不同项目的功能.
- 使用L1规范化的估计器同时识别隐性类和DIF项目,假设少量DIF项目.
- 为非平滑优化问题开发了一个计算效率高的预期最大化 (EM) 算法.
主要成果:
- 拟议的L1规范化方法有效地同时识别潜在类 (未知组) 和DIF项目.
- 模拟研究证明了该方法在各种场景中的性能.
- 该方法已成功应用于现实世界的教育测试数据,验证了其实际实用性.
结论:
- 开发的框架为DIF分析提供了一个强大的解决方案,在具有挑战性的环境中,缺少集团和项信息.
- 这种方法有助于评估教育和心理测试中的测量不变性和公平性.
- 这些发现有助于更公平,更可靠的测量仪器.
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