从纵向数据中的人内协会对人间差异的随时间变化进行分类
1Department of Psychological and Quantitative Foundations, College of Education, University of Iowa, Iowa City, IA, USA.
Multivariate behavioral research
|June 22, 2025
概括
纵向研究需要区分人与人之间的影响. 这项研究强调了需要在个人变化轨迹之间区分人与人之间的关系,以便进行准确的分析.
科学领域:
- 心理测量 心理测量 心理测量
- 量化心理学 量化心理学
- 纵向数据分析 纵向数据分析
背景情况:
- 纵向设计允许对随时间变量的关系进行检查.
- 区分人与人之间的关系 (平均差异) 和人内关系 (特定时间的残余) 对于时间变化的预测因素至关重要.
- 现有的方法往往忽略了个体变化轨迹之间的明显的人际关系.
研究的目的:
- 扩大对纵向数据分析的理解,通过引入区分单个斜率之间的人际关系.
- 在单变量和多变量纵向模型中展示这种区别的含义.
- 为使用纵向数据的研究人员提供实际建议.
主要方法:
- 使用模拟方法来说明拟议的区别.
- 分析使用单变纵向模型 (多层次/混合效应模型) 进行.
- 还使用多变量纵向模型 (结构方程模型) 进行了分析.
主要成果:
- 这项研究表明,未能区分单个斜率之间的人与人之间的关系会导致分析不准确.
- 模拟结果强调了这种区别在观察和潜变量模型中的重要性.
- 这些发现强调了纵向数据解释的复杂性.
结论:
- 研究人员必须从其他纵向效应中区分单个斜坡之间的人际关系.
- 提供了关于纵向数据分析最佳实践的建议.
- 讨论了纵向研究中关于滞效应模型的禁忌.
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