在回归分析中减少衰减偏差 涉及通过心理测量建模的评级尺度数据
Cees A W Glas1, Terrence D Jorgensen2, Debby Ten Hove3
1University of Twente.
Psychometrika
|February 25, 2026
概括
本研究引入了物品响应理论 (IRT) 和概括性理论 (GT) 模型的组合,以减少观察性研究中的测量误差. 这种方法提高了心理学和教育科学中的统计模型的准确性.
科学领域:
- 心理学 心理学 心理学
- 教育科学教育科学教育科学
- 测量理论 测量理论
背景情况:
- 心理学和教育方面的观察性研究经常使用多项评级表.
- 来自项目和评分器的测量误差减弱了回归系数,并降低了统计能力.
- 现有的 (层次) 线性模型容易受到这种误差差异的影响.
研究的目的:
- 提出一种新的建模程序,以减轻因测量错误引起的衰减.
- 提高观测研究中的统计分析的准确性和能力.
- 整合项目响应理论 (IRT) 和通用性理论 (GT) 进行增强的测量.
主要方法:
- 利用物品响应理论 (IRT) 模型将离散的物品响应转换为连续的潜伏规模.
- 采用了概括性理论 (GT) 模型,将隐性测量方差划分为感兴趣和烦方差的组件.
- 将IRT-GT综合测量集成到 (层次) 线性模型中,作为预测或标准变量.
主要成果:
- 演示了一种程序,有效地部分排除因干扰效应而产生的误差差异.
- 展示了IRT-GT混合模型在教育测量环境中的应用.
- 证实可以使用通用软件来实现这种先进的建模技术.
结论:
- 拟议的IRT-GT建模程序有效地减少了观察性研究中的测量误差.
- 这种方法提高了 (等级) 线性模型在心理学和教育研究中的有效性和力量.
- 该方法很实用,可以使用易于使用的统计软件来实现.
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