相互作用或不相互作用:将相互作用纳入线性回归模型的优缺点
Aljoscha Rimpler1, Henk A L Kiers2, Don van Ravenzwaaij2
1Department of Psychometrics and Statistics, University of Groningen, Grote Kruisstraat 2/1, Heymans Building, room 212, 9712 TS, Groningen, The Netherlands. aljoscharimpler@gmail.com.
Behavior research methods
|February 7, 2025
概括
在心理学研究中包括相互作用效应至关重要. 错误指定的线性回归模型可能会导致结果偏差,这凸显了在统计分析中需要理论证明的必要性.
科学领域:
- 心理学 心理学 心理学
- 统计 统计 统计 统计
- 量化方法 量化方法
背景情况:
- 相互作用效应在心理学研究中经常被研究.
- 然而,这些影响往往很小,导致复制问题.
- 统计模型的选择对结果的解释有重大影响.
研究的目的:
- 为了比较正确指定的线性回归模型与错误指定的线性回归模型的概括性和估计性.
- 调查模型错误规范对相互作用和简单效应的影响.
- 为了评估各种噪声水平,预测器相关性和样本大小的模型性能.
主要方法:
- 进行了一项模拟研究,比较了两个线性回归模型:一个计算相互作用,另一个省略它们.
- 9216条件是通过操纵噪声水平,预测器相关性和回归权重来创造的.
- 在6个样本大小 (N=25到1000) 中,为每个条件抽取了1000个样本,共计超过5500万次分析.
主要成果:
- 模型错误规范显著偏差回归估计,有时会逆转或取消简单的效应.
- 错误指定的模型显示样本和人口之间的差异较小,解释了差异.
- 正确规定的模型在人口层面上显示出优越的整体数据解释.
结论:
- 在选择统计模型时,理论考虑至关重要.
- 如果不合理地排除相互作用效应,可能会导致误导性结论.
- 研究人员必须为在分析中包含或排除相互作用术语提供明确的理由.
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