错误变量回归作为一种可行的方法来进行随机错误的测量中介分析:估计,有效性和易于使用的实施方法
Andrew F Hayes1, Paul D Allison2, Sean M Alexander3
1Haskayne School of Business, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada. andrew.hayes@ucalgary.ca.
Behavior research methods
|October 30, 2025
概括
错误变量回归 (EIV) 纠正因测量错误引起的调度分析偏差,与普通最小平方回归 (OLS) 不同. EIV提供了准确的调解效应估计,特别是当可变的可靠性是已知的.
科学领域:
- 行为科学 行为科学
- 统计建模 统计建模
- 心理测量 心理测量 心理测量
背景情况:
- 调解分析在行为科学中被广泛使用.
- 普通最小平方 (OLS) 回归是调解分析的常用方法.
- OLS回归易受变量测量误差的偏差的影响.
研究的目的:
- 引入误差变量回归 (EIV) 作为调度分析中OLS的替代方案.
- 为了证明EIV回归能够考虑随机测量误差.
- 为研究人员提供易于使用的EIV回归实现.
主要方法:
- 变量中的错误 (EIV) 回归分析.
- 与普通最小平方回归 (OLS) 的比较.
- 单指标隐性变量结构方程建模 (SEM) 用于验证.
- 小规模的模拟研究.
主要成果:
- 基于EIV回归的调解分析产生了与SEM相似的估计.
- OLS回归产生了对直接,间接和总效应的偏差估计.
- 在具有测量误差的模拟中,EIV回归成功恢复了调解模型参数.
结论:
- 在存在测量错误时,EIV回归是一种可行的,准确的OLS替代方案,用于调度分析.
- 研究人员应该使用像EIV回归这样的方法来考虑测量误差,以获得不偏见的估计.
- 在PROCESS宏观中的实现有助于采用EIV回归.
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