在密集的纵向数据中的时间尺度不匹配:基于动态结构方程模型的当前问题和可能的解决方案
Xiaohui Luo1, Yueqin Hu1, Hongyun Liu1
1Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education, Beijing Normal University, Faculty of Psychology.
Psychological methods
|May 19, 2025
概括
研究人员在密集的纵向数据中探索了动态关系,时间尺度不匹配. 像全路径和因子模型这样的改进模型准确地捕捉了这些复杂的相互作用,比旧方法提供了更好的方法指导.
科学领域:
- 心理学方法 心理学方法
- 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:
- 密集的纵向数据 (ILD) 对于研究变量之间的动态关系至关重要.
- 在ILD中变量之间的时间尺度不匹配是一个重要的分析挑战.
- 现有的动态结构方程建模 (DSEM) 方法,如部分路径和平均得分模型都有局限性.
研究的目的:
- 为了评估现有的DSEM模型对时间尺度不匹配的变量.
- 为了评估改进的DSEM方法的性能:全路径,因子和调整因子模型.
- 为分析具有时间尺度不匹配的ILD提供方法指南.
主要方法:
- 模拟研究 (研究1, 2-1, 2-2) 将不同条件下的模型性能进行比较.
- 评估部分路径,平均得分,全路径,因子和调整因子模型.
- 将模型应用于具有时间尺度不匹配变量的经验数据 (研究3).
主要成果:
- 与部分路径模型相比,全路径模型更好地捕捉了动态相互作用和特定时间效应.
- 因子模型为时间尺度不匹配的变量提供了准确的估计,与偏差平均得分模型不同.
- 当回归效应实质性时,调整后的因子模型比因子模型提供了边际改进.
结论:
- 时间尺度不匹配是ILD分析中的一个关键问题.
- 建议使用全路径和因子模型来分析与时间尺度不匹配的动态关系.
- 这项研究为ILD研究中的数据收集和分析策略提供了宝贵的见解.
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