使用反正弦变换对任何实验设计进行比例分析。
Analysis of proportions using arcsine transform with any experimental design.
作者信息
Laurencelle Louis, Cousineau Denis
机构信息
Département des sciences de l'activité physique, Université du Québec à Trois-Rivières, Trois-Rivières, QC, Canada.
École de psychologie, Université d'Ottawa, Ottawa, ON, Canada.
出版信息
Front Psychol. 2023 Jan 30;13:1045436. doi: 10.3389/fpsyg.2022.1045436. eCollection 2022.
INTRODUCTION
Exact tests on proportions exist for single-group and two-group designs, but no general test on proportions exists that is appropriate for any experimental design involving more than two groups, repeated measures, and/or factorial designs.
METHOD
Herein, we extend the analysis of proportions using arcsine transform to any sort of design. The resulting framework, which we have called (ANOPA), is completely analogous to the analysis of variance for means of continuous data, allowing the examination of interactions, main and simple effects, tests, orthogonal contrasts, et cetera.
RESULT
We illustrate the method with a few examples (single-factor design, two-factor design, within-subject design, and mixed design) and explore type I error rates with Monte Carlo simulations. We also examine power computation and confidence intervals for proportions.
DISCUSSION
ANOPA is a complete series of analyses for proportions, applicable to any design.
引言
针对单组和两组设计存在精确的比例检验，但不存在适用于任何涉及多于两组、重复测量和/或析因设计的实验设计的通用比例检验。
方法
在此，我们将使用反正弦变换的比例分析扩展到任何类型的设计。由此产生的框架，我们称之为（ANOPA），与连续数据均值的方差分析完全类似，允许检验交互作用、主效应和简单效应、检验、正交对比等。
结果
我们用几个例子（单因素设计、双因素设计、被试内设计和混合设计）来说明该方法，并通过蒙特卡罗模拟探索I型错误率。我们还研究了比例的功效计算和置信区间。
讨论
ANOPA是一系列完整的比例分析，适用于任何设计。
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