在单个案例研究中的计数数据分析中选择GLMM的模型:蒙特卡洛模拟
1Department of Educational Psychology, University of Minnesota, 56 E River Rd, Minneapolis, MN, 55455, USA. haoranli@umn.edu.
Behavior research methods
|July 10, 2024
概括
在单个案例实验设计 (SCED) 中,选择对计数数据的正确统计分布至关重要. 本研究建议针对泛型线性混合模型 (GLMMs) 进行特定的模型选择策略,以提高数据分析的准确性.
科学领域:
- 统计建模 统计建模
- 行为研究方法行为研究方法.
- 量化心理学 量化心理学
背景情况:
- 通用线性混合模型 (GLMM) 提供了先进的功能,用于分析单个案例实验设计 (SCED) 中的计数数据.
- 应用研究人员在为计数数据选择适当的统计分布时遇到挑战,特别是在处理过度分散和零通货膨胀时.
- 准确的统计决策对于可靠地解释SCED中的治疗效应至关重要.
研究的目的:
- 调查和提出有效的模型选择框架,用于为SCED计数数据在GLMM中选择合适的分布.
- 在SCED计数数据分析中解决过度分散和零通货膨胀的关键问题.
- 为应用研究人员提供基于证据的建议,帮助他们选择统计模型.
主要方法:
- 模拟研究涉及四种计数数据场景:Poisson,负二项式 (NB),零膨胀的Poisson (ZIP) 和零膨胀的负二项式 (ZINB).
- 提出了两个模型选择框架:一个使用信息标准 (AIC,BIC),另一个采用多阶段程序.
- 评估10种模型选择策略,评估偏差和对治疗效果估计和推断统计数据的影响.
主要成果:
- 模拟结果表明,在不同的数据场景中,模型选择策略的性能不同.
- 针对采用基于数据特征的特定模型选择方法,得出了具体的建议.
- 该研究确定了减轻模型选择偏差和提高治疗效果估计准确性的最佳策略.
结论:
- 拟议的模型选择框架和由此产生的建议为使用GLMM与SCED计数数据的研究人员提供了实际指导.
- 采用适当的模型选择策略对于SCED研究中可靠的统计推理至关重要.
- 这项研究有助于提高在单个案例实验设计中分析复杂计数数据的方法严格性.
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