斜率和分期:地板效应是否会在多层AR模型中引发偏差?
MohammadHossein M Haqiqatkhah1, Oisín Ryan1,2, Ellen L Hamaker1
1Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht, The Netherlands.
Multivariate behavioral research
|December 31, 2023
概括
心理数据中的地板效应可以在多层自动回归模型中产生错误的分期效应. 这项模拟研究表明,偏差取决于如何建模残余方差,这会影响精神病理学研究.
科学领域:
- 心理学 心理学 心理学
- 统计 统计 统计 统计
- 纵向数据分析 纵向数据分析
背景情况:
- 多级自回归模型用于心理学中的强度纵向数据.
- 经常观察到自回归参数和精神病理学 (阶段化效应) 之间的正相关性.
- 这种分阶段效应可能是由于经验数据中的地板效应而造成的工件,其特点是偏斜的分布.
研究的目的:
- 调查地板效应是否以及在多层自回归模型中导致错误结论的程度.
- 检查数据偏差对自回归参数估计的影响.
- 了解在地板效应存在时剩余方差假设的作用.
主要方法:
- 使用三种动态模型进行模拟研究,这些模型可以产生地板效应数据.
- 模拟的多层数据有不同的斜率,时间点数和案例.
- 分析模拟数据使用标准的多层自动回归模型的顺序1 (AR(1)).
主要成果:
- 在自回归参数中仅在使用随机剩余方差进行建模时才观察到正偏差.
- 使用固定剩余方差的建模导致负偏差.
- 偏差的程度受到偏差程度和病例数量的影响.
结论:
- 在多级自回归模型中,地板效应确实可以导致错误的结论 (分阶段效应).
- 关于残余方差的建模选择显著影响偏差的方向和大小.
- 这些发现对心理学研究中的数据收集和建模策略有意义,特别是在扭曲的情感时间序列数据中.
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