类内相关偏差的自举估计。

Journal of applied measurement Pub Date : 2020-01-01
Xiaofeng Steven Liu, Kelvin Terrell Pompey
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引用次数: 0

摘要

已知类内相关性的估计是有偏差的,但很少有分析方法来评估偏差的量。分析方法需要正态性假设来估计偏差。Bootstrap不需要这样的假设,因此,无论模型假设如何,都可以用来估计偏差。我们利用聚类自举来计算估计类内相关性的偏差。提供了一个著名的数据集来说明典型的类内相关研究设计中的偏差估计,并讨论了它对其他研究设计的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bootstrap Estimate of Bias for Intraclass Correlation.

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.

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