Omega squared (ω^2) is a measure of effect size for analysis of variance (ANOVA) designs. It is less biased than eta squared, but reported less often. This is in part due to lack of clear guidance on how to calculate it. In this paper, we discuss the logic behind effect size measures, the problem with eta squared, the history of omega squared, and why it has been underused. We then provide a user-friendly guide to omega squared and partial omega squared for ANOVA designs with fixed factors, including one-way, two-way, and three-way designs, using within-subjects factors and/or between-subjects factors. We show how to calculate omega squared using output from SPSS. We provide information on the calculation of confidence intervals. We examine the problems of nonadditivity, and intrinsic versus extrinsic factors. We argue that statistical package developers could play an important role in making the calculation of omega squared easier. Finally, we recommend that researchers report the formulas used in calculating effect sizes, include confidence intervals if possible, and include ANOVA tables in the online supplemental materials of their work. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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揭秘欧米茄平方：常见方差设计分析中效应大小的实用指南。

欧米伽平方 (ω^2) 是方差分析 (ANOVA) 设计的效应大小的度量。它的偏差小于 eta 平方，但报告频率较低。部分原因是缺乏关于如何计算的明确指导。在本文中，我们讨论了效应量测量背后的逻辑、eta 平方的问题、omega 平方的历史以及它未被充分利用的原因。然后，我们使用受试者内因素和/或受试者间因素，为具有固定因素的方差分析设计（包括单向、双向和三向设计）提供欧米茄平方和部分欧米茄平方的用户友好指南。我们展示了如何使用 SPSS 的输出来计算 omega 平方。我们提供有关计算置信区间的信息。我们研究非可加性以及内在因素与外在因素的问题。我们认为，统计包开发人员可以在简化欧米伽平方的计算方面发挥重要作用。最后，我们建议研究人员报告用于计算效应大小的公式，如果可能的话包括置信区间，并在其工作的在线补充材料中包括方差分析表。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。