Estimating effect size when there is clustering in one treatment group
Estimating effect size when there is clustering in one treatment group
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DOI：
10.3758/s13428-014-0538-z
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发表时间：
2015-12-01
影响因子：
5.4
通讯作者：
Citkowicz, Martyna
中科院分区：
文献类型：
--
作者：
Hedges, Larry V.;Citkowicz, Martyna
一些实验设计涉及仅在一个治疗组内进行聚类。这种设计可能涉及小组辅导、由多名治疗师进行的治疗或诊所对治疗组进行的干预，而对照组则不接受任何治疗。在这种情况下，数据分析通常会像治疗组内没有聚类一样进行。结果是治疗效果的实际显着性水平（即实际 p 值大于）名义水平。此外，在效应大小及其方差的估计中会引入偏差，当不考虑治疗组中的聚类时，会导致效应夸大和方差低估。聚类的这些后果可能会严重影响研究结果的解释。本文展示了如何使用类内相关性信息来校正效应大小及其方差的偏差，以及如何调整聚类效应的显着性检验。
Some experimental designs involve clustering within only one treatment group. Such designs may involve group tutoring, therapy administered by multiple therapists, or interventions administered by clinics for the treatment group, whereas the control group receives no treatment. In such cases, the data analysis often proceeds as if there were no clustering within the treatment group. A consequence is that the actual significance level of the treatment effects is larger (i.e., actual p values are larger) than nominal. Additionally, biases will be introduced in estimates of the effect sizes and their variances, leading to inflated effects and underestimated variances when clustering in the treatment group is not taken into account. These consequences of clustering can seriously compromise the interpretation of study results. This article shows how information on the intraclass correlation can be used to obtain a correction for biases in the effect sizes and their variances, and also to obtain an adjustment to the significance test for the effects of clustering.