在单个案例研究中使用零膨胀和过度分散的计数数据的多层模型
Haoran Li1, Wen Luo2, Eunkyeng Baek2
1Department of Educational Psychology, University of Minnesota, Minneapolis, MN, USA. haoranli@umn.edu.
零膨胀负二项式 (ZINB) 模型准确地估计了用零膨胀计数数据在单个案例实验设计 (SCED) 中的治疗效应. 其他模型显示有偏见的结果,突出了ZINBB.
科学领域:
- 行为科学 行为科学
- 统计建模 统计建模
- 心理测量 心理测量 心理测量
背景情况:
- 计数结果在单个案例实验设计 (SCED) 中很常见.
- 通用线性混合模型 (GLMM) 可以处理过分散的计数数据.
- 在SCED的基线数据中,零通货膨胀是一个重大的分析挑战.
研究的目的:
- 用多个基线设计 (MBD) 来解决SCED中零膨胀和过度分散的计数数据.
- 评估各种GLMM (Poisson,NB,ZIP,ZINB) 在SCED中估计治疗效果和推断统计数据的性能.
- 用现实世界的例子来展示这种数据的分析.
主要方法:
- 在MBD框架内模拟零膨胀和过分散的计数数据.
- 应用了四个GLMM:Poisson,负二项式 (NB),零膨胀的Poisson (ZIP) 和零膨胀的负二项式 (ZINB).
- 评估治疗效果估计的准确性和推断统计数据的可靠性.
主要成果:
- ZINB模型为零膨胀和过度分散的数据提供了准确的治疗效果估计.
- 当数据被零膨胀时,Poisson,NB和ZIP模型产生了偏差的估计.
- 当数据过度分散而不是零膨胀时,ZINB和ZIP模型的表现不佳.
结论:
- 建议使用 ZINB 模型来分析带有 MBD 的 SCED 中的零膨胀和过分散计数数据.
- 研究人员在为SCEDs选择统计模型时,应仔细考虑零通胀和过度分散的存在.
- 需要进一步的研究来探索替代方法,并解决在SCED中处理复杂计数数据结构的局限性.
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