教程:在横截面回归中的相互作用效应的功率分析
David A A Baranger1, Megan C Finsaas2, Brandon L Goldstein3
1Department of Psychiatry, Washington University in St. Louis.
Advances in methods and practices in psychological science
|August 13, 2025
概括
在回归中对相互作用效应进行功率分析是复杂的. R包InteractionPoweR简化了这一点,使研究人员能够轻松进行相互作用的功率分析,即使是与相关变量.
科学领域:
- 统计 统计 统计 统计
- 心理测量 心理测量 心理测量
- 量化心理学 量化心理学
背景情况:
- 相互作用分析,也称为调节或调节多重回归,评估两个变量之间的关系如何根据第三个变量变化.
- 对相互作用进行准确的功率分析具有挑战性,特别是在相关和连续变量方面,现有的软件往往缺乏灵活性.
- 影响统计能力的关键因素,如主要效应,它们的相关性和变量可靠性,并不总是被清楚地纳入功率分析.
研究的目的:
- 引入R包InteractionPoweR及其相关的Shiny应用程序,用于对交互效应进行功率分析.
- 为研究人员提供一个用户友好的工具,用于分析和基于模拟的功率分析,需要最小的编程经验.
- 为了证明主要效应,相关性,可靠性和变量分布等因素如何影响相互作用分析的统计能力.
主要方法:
- 使用R包InteractionPoweR进行相互作用效应的功率分析.
- 在包中采用基于分析和仿真的方法.
- 证明使用诸如皮尔森相关性,样本大小,可靠性和变量分布 (例如二进制,利克特尺度) 等参数.
主要成果:
- 交互功率 (InteractionPoweR) 软件包可方便对交互效应进行功率分析,并容纳相关变量和连续变量.
- 该教程说明了主要效应,它们的相关性,变量可靠性和分布如何影响统计能力.
- 该套件允许灵活结合各种参数,以提高功率估计的准确性.
结论:
- R包InteractionPoweR为研究人员提供了一种有价值和易于使用的工具,用于对相互作用效应进行强大的功率分析.
- 了解主要效应,相关性和可靠性的影响对于在温和回归中进行准确的功率分析至关重要.
- 该套件使研究人员能够更好地规划研究,并解释涉及统计模型相互作用效应的发现.
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