在单个案例实验设计中的计数和速率数据分析中使用通用线性混合模型:一步一步的教程
Haoran Li1, Eunkyeng Baek2, Wen Luo2
1University of Minnesota, USA.
Evaluation & the health professions
|December 11, 2024
概括
通用线性混合模型 (GLMM) 为单个案例实验设计 (SCED) 提供了先进的分析. 本教程展示了使用GLMM用于SCED计数和速率数据,支持自闭症儿童的语言前环境教学有效性.
科学领域:
- 行为科学 行为科学
- 统计建模 统计建模
- 发展心理学 发展心理学
背景情况:
- 单个案例实验设计 (SCED) 可以产生有价值的数据,但往往需要先进的统计方法.
- 通用线性混合模型 (GLMM) 是用于分析SCED中常见的计数和速率数据的强大工具.
- 应用研究人员可能会发现实施GLMMs具有挑战性,需要实际指导.
研究的目的:
- 为单个案例实验设计 (SCED) 数据应用通用线性混合模型 (GLMMs) 提供一个教程.
- 用GLMMs.To展示一个逐步的程序来分析使用GLMMs.Count和Rate的结果.
- 为了说明GLMM的应用,使用实证示例来检查语言前环境教学 (PMT).
主要方法:
- 利用了来自接受语言前环境教学 (PMT) 的六名自闭症学龄儿童的经验数据集.
- 分析了使用GLMMs持续的故意通信 (频率计数) 和启动的故意通信 (速率) 的结果.
- 提供相关的R和SAS代码,用于逐步分析程序.
主要成果:
- GLMM分析支持了关于语言前环境教学 (PMT) 有效性的原始发现.
- GLMMs提供了对个别治疗效果和病例间变化的精确估计.
- 解释了GLMM发现与原始研究结论之间的相似之处和差异.
结论:
- GLMMs提供了一种强大的方法来分析SCED中的计数和速率数据,提高对治疗效应的理解.
- 在这项研究中,GLMM的应用证实了PMT在改善自闭症儿童的语言前沟通方面的有效性.
- 这项工作为研究人员提供了实用的指导和代码,以便在SCED研究中实施先进的统计分析.
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