Using Power Tables to Compute Statistical Power in Multilevel Experimental Designs
Using Power Tables to Compute Statistical Power in Multilevel Experimental Designs
复制标题
使用功效表计算多级实验设计中的统计功效
DOI：
10.7275/xdz3-p084
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发表时间：
2009
影响因子：
--
通讯作者：
S. Konstantopoulos
中科院分区：
文献类型：
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作者：
S. Konstantopoulos
假定简单的随机样本的单级实验设计的功率计算大大简化了功率表，例如科恩关于统计功率分析的书中提出的那些功率表。然而，在教育和社会科学中，实验设计具有自然的嵌套结构，需要多水平模型来正确计算治疗效果检验的威力。这样的功率计算可能需要一些统计软件的编程和特殊例程。或者，可以使用典型的功率表来计算嵌套设计中的功率。本文提供了使用典型的功率表来定义嵌套设计中计算功率所需的预期效应大小和样本大小的简单公式。文中给出了一些简单的例子来说明公式的有效性。
Power computations for one-level experimental designs that assume simple random samples are greatly facilitated by power tables such as those presented in Cohen’s book about statistical power analysis. However, in education and the social sciences experimental designs have naturally nested structures and multilevel models are needed to compute the power of the test of the treatment effect correctly. Such power computations may require some programming and special routines of statistical software. Alternatively, one can use the typical power tables to compute power in nested designs. This paper provides simple formulae that define expected effect sizes and sample sizes needed to compute power in nested designs using the typical power tables. Simple examples are presented to demonstrate the usefulness of the formulae