在Thurstonian IRT模型中估计和使用块信息
1University of Mannheim, Mannheim, Germany. frick@statistik.tu-dortmund.de.
Psychometrika
|August 28, 2023
概括
本研究介绍了多维强制选择 (MFC) 测试中的瑟斯顿项目响应模型 (Thurstonian IRT) 的块信息估计. 区块信息通过计算依赖关系来改善可靠的MFC测试的构建.
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:
- 多维强制选择 (MFC) 测试越来越受欢迎,但构建起来很复杂.
- 瑟斯顿的项目响应模型 (Thurstonian IRT) 常用于对具有主导项目的MFC测试进行评分.
- 目前的Thurstonian IRT评分的频率主义方法忽略了对对比中的随机依赖.
研究的目的:
- 在 Thurstonian IRT 模型中开发一种估计区块级别的费舍尔信息的方法.
- 与传统方法相比,使用块信息调查标准错误的准确性.
- 展示多维块信息如何总结以帮助MFC测试构建.
主要方法:
- 对Thurstonian IRT模型的区块级别的费舍尔信息的估计.
- 模拟研究比较基于块信息的观察和预期标准错误.
- 对忽略对标准错误估计的局部依赖性的影响分析.
- 用于实际测试构建的多维块信息的总结.
主要成果:
- 基于块信息的观察和预期标准错误显示了类似的准确性.
- 忽视局部依赖导致低估的标准误差,除非使用最大后期估计器.
- 根据模拟和经验应用中考虑的结果,块信息总结显示出小差异.
结论:
- 在区块层面估计费舍尔信息为MFC测试提供了准确的评分方法.
- 计算依赖关系的块信息有助于构建更可靠的MFC测试.
- 拟议的方法为心理测量学家和测试开发人员提供了一个有价值的工具.
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