在单个案例研究中使用数量和比例数据进行多层次建模:演示和评估
Haoran Li1, Wen Luo1, Eunkyeng Baek1
1Department of Educational Psychology, Texas A&M University.
Psychological methods
|August 21, 2023
概括
本研究介绍了一般化的线性混合模型 (GLMMs),用于分析单个案例实验设计 (SCEDs) 中的计数和比例数据. 这项研究提供了实际指导和基于模拟的建议,以有效地应用GLMM.
科学领域:
- 统计 统计 统计 统计
- 行为研究方法 行为研究方法
背景情况:
- 单个案例实验设计 (SCED) 经常产生计数或比例结果数据.
- 现有的统计方法可能无法完全捕捉这些数据的复杂性.
研究的目的:
- 介绍和说明一类新的通用线性混合模型 (GLMMs),用于分析SCED中的计数和比例数据.
- 为研究人员提供关于应用GLMM的实际指导,包括过度分散,估计和解释等方面.
主要方法:
- 对于计数和比例数据的GLMM的口头插图.
- 详细讨论GLMM框架组件:过度分散,估计,推断,模型选择和系数解释.
- 实证示例和模拟研究来评估GLMM的性能.
主要成果:
- 模拟研究评估了GLMM在偏差和治疗效应 (即时和趋势) 覆盖率方面的表现.
- 研究了统计测试的实证I型错误率.
- 结果为在SCED研究中使用GLMM时的统计决策提供了基础.
结论:
- 在SCED中,GLMM为分析计数和比例数据提供了一个强大的框架.
- 该研究为SCED研究人员提供了必要的信息,并概述了方法学家的未来方向.
- 在实施GLMMs时,提供了关于健全统计决策的建议.
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