Power struggles: Estimating sample size for multilevel relationships research
Power struggles: Estimating sample size for multilevel relationships research
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DOI：
10.1177/0265407517710342
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发表时间：
2018-01-01
影响因子：
2.8
通讯作者：
Hennes, Erin P.
中科院分区：
文献类型：
--
作者：
Lane, Sean P.;Hennes, Erin P.
对人际关系进行研究需要设计和分析的特殊挑战。许多重要的问题受益于研究的二元和家庭，在自然环境中的关系的研究往往涉及纵向和/或集群设计。反过来，这些研究的功效分析需要额外的考虑，因为多水平统计模型（或结构方程模型等效物）通常用于分析关系数据。多水平模型中的功效计算涉及为大量参数指定假设值的困难任务。规划研究还可能涉及功率权衡，包括是否优先考虑采样的二元组数量或每个二元组的重复测量数量。不幸的是，关于如何处理这些问题的关系文献提供的指导有限。在这篇文章中，我们提出了一种数据模拟方法，用于估计常用关系研究设计的功效。我们还说明了使用两个工作的关系研究的例子的方法。
Conducting research on human relationships entails special challenges of design and analysis. Many important questions benefit from the study of dyads and families, and studies of relationships in natural settings often involve longitudinal and/or clustered designs. In turn, power analyses for such studies require additional considerations, because multilevel statistical models (or structural equation modeling equivalents) are often used to analyze relationships data. Power calculations in multilevel models involve the difficult task of specifying hypothesized values for a large number of parameters. Planning studies can also involve power trade-offs, including whether to prioritize the number of dyads sampled or the number of repeated measurements per dyad. Unfortunately, the relationships literature provides limited guidance on how to deal with these issues. In this article, we present a data simulation method for estimating power for commonly used relationships research designs. We also illustrate the method using two worked examples from relationships research.