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让睡着的狗躺在那里吗? 如何处理夜间差距问题在经验采样方法数据数据数据
Sophie W Berkhout1, Noémi K Schuurman1, Ellen L Hamaker1
1Department of Methodology and Statistics, Utrecht University.
Psychological methods
|May 22, 2025
概括
经验采样方法 (ESM) 中的夜间差距数据影响滞后变量分析. 这项研究阐明了常见的处理方法,并提出了一种新的方法,揭示了最佳的夜间间隙建模因变量而异.
科学领域:
- 心理学方法 心理学方法
- 量化心理学 量化心理学
- 行为科学 行为科学
背景情况:
- 经验采样方法 (ESM) 的数据收集本质上包括每日测量之间的"夜间间隙".
- 这些夜间间间隙对分析滞后关系 (自回归,交叉滞后回归) 的影响往往被忽视.
- 处理夜间间隙的现有方法包括忽略它们,删除它们或将它们视为缺失数据.
研究的目的:
- 在第一阶级自回归模型中明确详细说明处理夜间间隙的三种常见方法的理论含义.
- 引入一个替代的建模框架,以便对夜间间隙效应进行更细致的研究.
- 实证测试哪种夜间间隙处理方法最适合ESM数据中的不同变量.
主要方法:
- 在自动回归模型中,对三种已建立的夜间间隙处理技术进行理论分析.
- 开发和提出一种新的,更详细的模拟方法,用于夜间间隙.
- 使用N=1设计与多个ESM变量进行实证应用,以比较模型匹配.
主要成果:
- 该研究表明,常见的夜间间隙处理方法是拟议的替代方法的特殊情况.
- 经验发现表明,模拟夜间间隙的最佳方法是变量依赖的.
- 这种变化表明,与白天相比,心理过程在夜间可能表现出不同的动态.
结论:
- 在ESM数据中处理夜间差距的方法选择对滞后关系的解释产生重大影响.
- 一个适合所有人的方法是不够的;变量特定的建模是必要的,以准确分析ESM数据.
- 这项研究为在ESM研究中更复杂地理解和建模夜间差距提供了基础.
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