在纵向设计中推断人内关系的两步稳健估计方法:教程和模拟
1Department of Education, University of Tokyo, Bunkyo-ku, Tokyo, Japan.
Multivariate behavioral research
|December 27, 2025
概括
这项研究引入了一种新的两步方法,以将个人内部的变化与稳定的特征分开. 这种方法增强了心理研究中的因果参数估计,特别是在纵向数据中.
科学领域:
- 心理学研究方法 心理学研究方法
- 纵向数据分析的数据分析.
- 因果推理的原因推理.
背景情况:
- 从人与人之间的差异中分离个人内部的变异性是心理学研究的一个关键挑战.
- 现有的方法可能会与复杂的关系和未观察到的混因素作斗争.
研究的目的:
- 介绍和演示一种新的两步方法来分类人内变异性.
- 提供教程,模拟和拟议方法的示例.
- 为了提高心理研究中的因果参数估计.
主要方法:
- 一个包含结构方程建模的两步程序,用于预测人内可变性得分 (WPVS).
- 使用潜在结果方法估计因果参数,特别是结构嵌套平均模型 (SNMMs).
- 通过使用纵向数据 (T>=4) 的大规模模拟来调查估计性能.
主要成果:
- 拟议的方法允许灵活地包含WPVS的曲线和相互作用效应.
- 它为相互关系提供了更准确的因果参数估计,即使有未观察到的混因素.
- 该方法降低了不适当解决方案的风险,并且不需要对时间变化的混因子进行模型.
结论:
- 这种新方法有效地分解了个人内部的变异性,并改善了因果推理.
- 它在各种条件下表现良好,具有足够的纵向数据.
- 该方法以东京青少年队列 (TTC) 研究的一个例子来说明.
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