Statistical Power of Experimental Research with Hierarchical Data
Statistical Power of Experimental Research with Hierarchical Data
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DOI：
10.2333/bhmk.38.63
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
S. Usami
中科院分区：
文献类型：
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作者：
S. Usami
当单元(例如，学生)的数据嵌套在各种集群(例如，班级和学校)中时，层次化数据集出现，并且经常出现在行为研究中。估计统计能力和样本量要求是数据收集中的基本问题之一，特别是在实验研究中，有时获取大样本是不现实的。在本研究中，我们讨论了在具有分层数据的实验研究中评估检验干预效果的统计能力的一般程序，主要集中在受试者之间的双向设计上。这种方法使得能够通过使用基于Wald统计的多参数检验来评估关于主效应和交互效应的各种类型对比的统计能力。此外，还给出了几个数值例子，说明了不同样本大小、干预效应大小、组内相关性和某些数据假设条件下，各种对比的统计能力是如何变化的。讨论了所提出方法的扩展和实际应用中的问题。
Hierarchical data sets arise when data for units (e.g., students) are nested within various clusters (e.g., classes and schools), and often appear in behavioral research. Estimating statistical power and sample size requirements is one of the fundamental questions in data collection, especially in experimental research where obtaining large samples is sometimes unrealistic. In the present research, we discuss a general procedure for evaluating statistical power to test intervention effects in experimental research with hierarchical data, focusing mainly on a two-way between-subjects design. This approach enables the statistical power of various types of contrasts to be evaluated with respect to main effects and interaction effects by using multiparameter tests based on Wald statistics. Additionally, several numerical examples are presented to show how the statistical power for various contrasts changes with various values of sample size, sizes of intervention effects, intraclass correlation and some data assumptions. Extensions of the proposed method and issues for practical applications are noted in discussion.