How Many Participants Do We Have to Include in Properly Powered Experiments? A Tutorial of Power Analysis with Reference Tables.
How Many Participants Do We Have to Include in Properly Powered Experiments? A Tutorial of Power Analysis with Reference Tables.
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DOI：
10.5334/joc.72
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发表时间：
2019-07-19
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通讯作者：
Brysbaert, Marc
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文献类型：
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作者：
Brysbaert, Marc
鉴于d = .4的效应量是心理学研究中感兴趣的最小效应量的一个很好的初步估计，如果我们想以80%的把握度进行研究，我们已经需要超过50名参与者来简单比较两个参与者内的条件。这超过了目前的做法。此外，一旦涉及组间变量或交互作用，则需要100、200甚至更多的参与者。只要我们不接受这些事实，我们将继续进行动力不足的研究，结果不明确。要解决这个问题，需要改变主管、审查员、审稿人和编辑对研究的评价方式。本文介绍了心理学家最常用的设计所需的参考编号，包括两水平和三水平的单变量组间设计和重复测量设计，涉及两个重复测量变量或一个组间变量和一个重复测量变量的双因素设计（裂区设计）。这些数字是针对传统的频率分析给出的，p << .05 and Bayesian analysis with BF >10。这些数字为研究人员提供了一个标准，以确定（和证明）即将进行的研究的样本量。文章还描述了研究人员如何通过对每个参与者的每个条件进行多个观察来提高他们的研究能力。
Given that an effect size of d = .4 is a good first estimate of the smallest effect size of interest in psychological research, we already need over 50 participants for a simple comparison of two within-participants conditions if we want to run a study with 80% power. This is more than current practice. In addition, as soon as a between-groups variable or an interaction is involved, numbers of 100, 200, and even more participants are needed. As long as we do not accept these facts, we will keep on running underpowered studies with unclear results. Addressing the issue requires a change in the way research is evaluated by supervisors, examiners, reviewers, and editors. The present paper describes reference numbers needed for the designs most often used by psychologists, including single-variable between-groups and repeated-measures designs with two and three levels, two-factor designs involving two repeated-measures variables or one between-groups variable and one repeated-measures variable (split-plot design). The numbers are given for the traditional, frequentist analysis with p < .05 and Bayesian analysis with BF > 10. These numbers provide researchers with a standard to determine (and justify) the sample size of an upcoming study. The article also describes how researchers can improve the power of their study by including multiple observations per condition per participant.