Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs.
Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs.
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DOI：
10.3389/fpsyg.2013.00863
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发表时间：
2013-11-26
影响因子：
3.8
通讯作者：
Lakens D
中科院分区：
文献类型：
--
作者：
Lakens D
效应大小是经验研究的最重要结果。大多数作用尺寸的文章都强调了它们对传达结果的实际意义的重要性。对于科学家本身而言，效应大小最有用，因为它们促进了累积科学。效应大小可用于确定样本量以进行随访研究或研究跨研究的效果。本文旨在提供有关如何计算和报告t检验和ANOVA的效果大小的实用入门，以便在A-Priori功率分析和荟萃分析中使用效果大小。尽管有关效果大小的许多文章仅关注受试者之间的设计和地址内部设计，但我提供了详细的概述，详细概述了对象内部和受试者之间设计之间的相似性和差异。我建议，实验心理学中的一些研究问题检查了固有的个体内部效应，这使得效应大小结合了衡量结果之间的相关性是结果的最佳摘要。最后，提供了一个补充电子表格，以使研究人员尽可能容易地将效果尺寸计算纳入其工作流程中。
Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they facilitate cumulative science. Effect sizes can be used to determine the sample size for follow-up studies, or examining effects across studies. This article aims to provide a practical primer on how to calculate and report effect sizes for t-tests and ANOVA's such that effect sizes can be used in a-priori power analyses and meta-analyses. Whereas many articles about effect sizes focus on between-subjects designs and address within-subjects designs only briefly, I provide a detailed overview of the similarities and differences between within- and between-subjects designs. I suggest that some research questions in experimental psychology examine inherently intra-individual effects, which makes effect sizes that incorporate the correlation between measures the best summary of the results. Finally, a supplementary spreadsheet is provided to make it as easy as possible for researchers to incorporate effect size calculations into their workflow.