随机项目斜率回归:一种替代的测量模型,可以考虑与单个项目相关的相似性和差异
Ed Donnellan1, Satoshi Usami2, Kou Murayama3
1Department of Experimental Psychology, University College London.
Psychological methods
|July 27, 2023
概括
研究人员可以使用随机项目斜率回归来更准确地分析心理数据. 这种方法解释了独立变量与个别测量项的不同关系,防止统计分析中的膨胀型I错误.
科学领域:
- 心理学 心理学 心理学
- 统计 统计 统计 统计
- 量化研究方法 量化研究方法
背景情况:
- 心理学研究经常使用来自多项测量的依赖变量 (DV) 的复合分数.
- 传统的聚合或共同因子模型可能会忽视独立变量 (IV) 和单个项目之间的不同关系.
研究的目的:
- 引入和验证混合效应模型,随机项目斜率回归,作为替代测量模型.
- 证明未解决的随机项目斜率对统计推理的影响,特别是I型错误率.
- 提供一种方法来解释心理研究中的项目异质性.
主要方法:
- 开发了一种混合效应模型:随机项目斜率回归.
- 利用数学证明和模拟来评估I型错误率.
- 分析了来自调查和反应时间任务的现实数据 (n=564).
主要成果:
- 随机的项目斜率可以膨胀I型错误率,特别是在更大的样本大小.
- 在现实世界的心理数据中检测到有问题的随机项目斜率水平.
- 标准的统计指数不足以诊断随机项目斜率的存在.
结论:
- 随机项目斜率回归为分析异质项目的共同因子模型提供了有价值的替代方案.
- 研究人员必须考虑随机项的斜率,以确保准确的统计结论.
- 未来的研究应该探索用于识别随机项目斜率的诊断工具.
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