拆分还是不拆分:在多层模型中关注共变量
Remus Mitchell1, Craig K Enders1, Yi Feng1
1Department of Psychology, University of California, Los Angeles.
Multivariate behavioral research
|March 9, 2026
概括
在多层模型中分解1级共变量可以改善解释并减少偏差. 然而,在某些情况下,非分类方法可能会提供更高的精度,这会影响2级预测器估计的准确性.
科学领域:
- 多层次建模的多层次建模
- 统计方法学的统计方法.
- 量化心理学 量化心理学
背景情况:
- 对于实质性重要性,建议对1级变量进行分类.
- 对于分类1级共变量,平衡可解释性和减少偏差,共识是混合的.
- 如果共变量缺乏实质性兴趣,则可以认为分类是不必要的.
研究的目的:
- 探索在1级共变量分解中偏差-精度权衡.
- 当主要利益是2级预测器时,调查这些权衡.
- 提供关于在多层模型中处理较低级别共变量的最佳实践的见解.
主要方法:
- 蒙特卡洛模拟研究.
- 检查影响二级估计偏差,精度和功率的因素.
- 分析类内相关性,上下文效应大小,效应大小,二级效应之间的相关性,样本大小和分类方法 (显而易见与隐藏).
主要成果:
- 分解通常可以提高解释性,并减少1级共变量的偏差.
- 在非分类方法为2级估计提供更高的精度的情况下,存在特殊条件.
- 选择分类方法 (明显与隐藏) 影响结果.
结论:
- 对1级共变量进行分类具有好处,但需要仔细考虑精确性权衡.
- 结果为管理多层次分析中较低级别的共变量提供了最佳策略.
- 最佳实践应该平衡可解释性收益与潜在的精度损失.
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