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基于实证的实验室攻击研究的功效指南。

An empirically based power primer for laboratory aggression research.

机构信息

Department of Psychology, University of Georgia, Athens, Georgia, USA.

National Center for PTSD, Behavioral Science Division, VA Boston Healthcare System, Boston, Massachusetts, USA.

出版信息

Aggress Behav. 2022 May;48(3):279-289. doi: 10.1002/ab.21996. Epub 2021 Oct 5.

Abstract

Recent reviews suggest that, like much of the psychological literature, research studies using laboratory aggression paradigms tend to be underpowered to reliably locate commonly observed effect sizes (e.g., r = .10-.20, Cohen's d = ~0.20-0.40). In an effort to counter this trend, we provide a "power primer" that laboratory aggression researchers can use as a resource when planning studies using this methodology. Using simulation-based power analyses and effect size estimates derived from recent literature reviews, we provide sample size recommendations based on type of research question (e.g., main effect vs. two-way vs. three-way interactions) and correlations among predictors. Results highlight the large number of participants that must be recruited to reach acceptable (80%) power, especially for tests of interactions where the recommended sample sizes far exceed those typically employed in this literature. These discrepancies are so substantial that we urge laboratory aggression researchers to consider a moratorium on tests of three-way interactions. Although our results use estimates from the laboratory aggression literature, we believe they are generalizable to other lines of research using behavioral tasks, as well as psychological science more broadly. We close by offering a series of best practice recommendations and reiterating long-standing calls for attention to statistical power as a basic element of study planning.

摘要

最近的评论表明,与许多心理学文献一样,使用实验室攻击范式的研究往往没有足够的效力来可靠地定位常见的效应大小(例如,r=.10-.20,Cohen 的 d=0.20-0.40)。为了应对这一趋势,我们提供了一个“功效入门”,实验室攻击研究人员可以在使用这种方法计划研究时将其作为资源。我们使用基于模拟的功效分析和从最近的文献综述中得出的效应大小估计,根据研究问题的类型(例如,主效应与双向与三向交互作用)以及预测因素之间的相关性,提供样本量建议。结果突出显示了必须招募大量参与者才能达到可接受的(~80%)功效,特别是对于交互作用的测试,建议的样本量远远超过了该文献中通常使用的样本量。这些差异非常大,以至于我们敦促实验室攻击研究人员暂停对三向交互作用的测试。尽管我们的结果使用了实验室攻击文献中的估计值,但我们相信它们可以推广到其他使用行为任务的研究领域,以及更广泛的心理学科学。最后,我们提出了一系列最佳实践建议,并再次呼吁关注统计功效作为研究计划的基本要素。

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