如何从计划的不完整数据中估计Interrater可靠性的类内相关系数
Debby Ten Hove1, Terrence D Jorgensen2, L Andries Van der Ark2
1Faculty of Behavioural and Movement Sciences, Section of Educational Sciences, LEARN! Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Multivariate behavioral research
|June 17, 2025
概括
本研究比较了用于计算缺失值的观测数据的类内相关系数 (ICC) 的方法. 建议对随机效应模型进行最大概率估计,以便在行为研究中准确和可行的ICC估计.
科学领域:
- 行为科学 行为科学
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
背景情况:
- 测量器间可靠性 (IRR) 对观测数据至关重要,通常使用类内相关系数 (ICC) 进行评估.
- 使用ANOVA的传统ICC估计方法存在不完整数据的问题,这是计划缺失的观测设计中常见的.
- 行为研究经常采用计划中的缺失设计,需要对不完整的数据集采用强大的ICC估计技术.
研究的目的:
- 为了比较计划不完整的观测数据的三个新型ICC估计方法的计算准确性和可行性.
- 确定在行为研究背景下缺少数据的情况下估计ICC的最可靠方法.
主要方法:
- 模拟计划的不完整数据以模仿现实世界的观察研究.
- 评估了三种估计方法:贝叶斯层次线性模型 (MCMC),随机效应模型的最大概率 (ML) 和共同因素模型的ML.
- 评估计算准确性 (偏差,RMSE,覆盖范围) 和可行性 (融合,时间).
主要成果:
- 随机效应模型的最大概率估计显示在所有评估标准中表现优异.
- 与其他方法相比,这种方法在点和可变性估计中显示出更高的准确性和更高的覆盖率.
- 该研究为这些先进的ICC估计技术的实际应用提供了R代码.
结论:
- 随机效应模型的最大概率估计,特别是蒙特卡洛置信区间,是使用不完整的观测数据进行ICC估计的首选方法.
- 这些发现为行为科学研究人员提供了实际指导,这些研究人员应对计划中的缺失数据.
- 随着R代码的可用性,这些改进的统计方法在未来的研究中更容易实施.
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