一种隐性变量混合效应的位置尺度模型,它还考虑了在自相关性中人与人之间的差异
Steffen Nestler1, Shelley A Blozis2
1Institut für Psychologie, Universität Münster, Münster, Germany.
Statistics in medicine
|November 6, 2023
概括
本研究引入了一种先进的混合效应模型,用于计算密集纵向数据中的测量误差. 新模型允许残余方差和自回归过程中的个体差异,增强经验采样和日常日记研究的分析.
科学领域:
- 公共卫生研究 公共卫生研究
- 心理学 心理学 心理学
- 统计 统计 统计 统计
背景情况:
- 从经验采样和日常日记设计中获得的密集的纵向数据在公共卫生中越来越普遍.
- 混合效应模型和混合效应位置尺度模型通常用于分析这些数据.
- 现有的模型可能无法完全解释测量误差或动态过程中的个体变化.
研究的目的:
- 引入混合效应位置尺度模型的扩展,通过隐性因子模型结合测量误差.
- 为了允许隐性因子的残余方差和自回归过程的个体特异性差异.
- 提供最大概率估计方法,并将其性能与贝叶斯方法进行比较.
主要方法:
- 开发一个扩展的混合效应位置尺度模型,其中包含测量误差的潜在因子模型.
- 使用最大概率方法估计模型参数.
- 通过模拟研究对最大概率与贝叶斯估计方法进行比较.
主要成果:
- 拟议的扩展混合效应位置规模模型有效考虑测量误差.
- 该模型允许潜在因子残余方差和自动回归参数在人与人之间具有显著的可变性.
- 模拟结果表明,最大概率估计方法的表现良好.
结论:
- 增强的混合效应位置尺度模型为分析具有测量误差的密集纵向数据提供了强大的工具.
- 该模型提供了对动态心理过程中个体差异的更细致的理解.
- 未来的研究可以扩展这一框架,以解决更复杂的纵向数据结构和研究问题.
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