Liu Xiaofeng Steven, Pompey Kelvin Terrell
Xiaofeng Steven Liu, Department of Educational Studies, University of South Carolina, Columbia, SC 29208, USA,
J Appl Meas. 2020;21(1):101-108.
The estimates of intraclass correlations are known to be biased, but there are few analytical ways to assess the amount of bias. The analytical approach requires the normality assumption to estimate bias. Bootstrap requires no such assumption and can, therefore, be used to estimate bias, regardless of the model assumption. We utilize cluster bootstrapping to calculate the bias in estimating the intraclass correlation. A well-known dataset is provided to illustrate the bias estimation in a typical study design of intraclass correlation, and its implications for other study designs are also discussed.
众所周知，组内相关系数的估计存在偏差，但评估偏差量的分析方法却很少。分析方法需要正态性假设来估计偏差。而自助法不需要这样的假设，因此，无论模型假设如何，都可用于估计偏差。我们利用聚类自助法来计算估计组内相关系数时的偏差。提供了一个著名的数据集来说明在典型的组内相关研究设计中的偏差估计，并讨论了其对其他研究设计的影响。
