Computing contrasts, effect sizes, and counternulls on other people's published data: General procedures for research consumers
Computing contrasts, effect sizes, and counternulls on other people's published data: General procedures for research consumers
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DOI：
10.1037/1082-989x.1.4.331
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发表时间：
1996-12-01
影响因子：
7
通讯作者：
Rosenthal, R
中科院分区：
文献类型：
--
作者：
Rosnow, RL;Rosenthal, R
我们描述了方便的统计程序，使研究消费者（例如专业心理学家、研究生和研究人员本身）能够超越已发表的结论并对报告的结果进行独立评估。适当构思的对比以及效应大小估计通常使研究人员能够解决已发表报告的作者可能过早忽略或放弃的精确预测。我们描述了使用 t、F 和 Z 来计算与不同原材料的对比，并回顾了 3 个效应大小指数（Cohen's d、Hedges's g 和 Pearson r）以及显示任何效应大小 r 大小的方法。我们还描述了如何构建所获得效果的置信限及其零反零区间。
We describe convenient statistical procedures that will enable research consumers (e.g., professional psychologists, graduate students, and researchers themselves) to reach beyond the published conclusions and make an independent assessment of the reported results. Appropriately conceived contrasts accompanied by effect size estimates often allow researchers to address precise predictions that the authors of the published report may have ignored or abandoned prematurely. We describe the use of t, F, and Z to compute contrasts with different raw ingredients, and we review 3 effect size indices (Cohen's d, Hedges's g, and the Pearson r) and a way of displaying the magnitude of any effect size r. We also describe how to construct confidence limits for the obtained effect as well as its null-counternull interval.