在单个案例中分析计数数据,使用通用线性混合模型进行实验设计:序列依赖是否重要?
1Department of Educational Psychology, University of Minnesota, Minneapolis, United States.
Multivariate behavioral research
|October 1, 2025
概括
通用线性混合模型 (GLMMs) 可以用计数数据分析单个案例实验设计 (SCEDs),即使有自相关性. 本研究评估了强大的GLMM和具有自回归误差的新模型,以准确估计治疗效果.
科学领域:
- 统计建模 统计建模
- 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) 提供了有价值的治疗效果见解.
- 在SCED数据中的自相关性可能会违反标准统计模型的假设.
- 一般化的线性混合模型 (GLMM) 越来越多地用于SCED计数数据.
研究的目的:
- 评估现有GLMM对自相关的SCED计数数据的稳定性.
- 评估包含自回归错误的新型GLMM和线性混合模型 (LMM).
- 为分析有序依赖的SCED计数数据提供指导.
主要方法:
- 蒙特卡洛模拟研究.
- 偏差,覆盖率和I型错误率的评估.
- 对现实世界SCED计数数据的分析.
主要成果:
- 以前使用的GLMM显示出不同强度的自相关性.
- 具有自动回归结构的新型GLMM和LMM显示性能有所改善.
- 制定了处理SCED计数数据分析中的自相关性建议.
结论:
- 在分析SCED计数数据时,必须考虑自相关性.
- 包含自回归错误的模型提供了一个可行的解决方案.
- 需要进一步的研究来完善SCED数据分析的方法.
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