样本量测定
二元分析
斯皮尔曼秩相关系数
统计
皮尔逊积矩相关系数
样品(材料)
相关系数
计算机科学
相关性
推论
统计推断
数据挖掘
数学
人工智能
化学
几何学
色谱法
作者
Justine O. May,Stephen W. Looney
出处
期刊:Journal of biometrics & biostatistics
[OMICS Publishing Group]
日期:2020-01-01
卷期号:11 (6): 1-7
被引量:30
标识
DOI:10.37421/2155-6180.2020.11.440
摘要
Bivariate correlation analysis is one of the most commonly used statistical methods. Unfortunately, it is generally the case that little or no attention is given to sample size determination when planning a study in which correlation analysis will be used. For example, our review of clinical research journals indicated that none of the 111 articles published in 2014 that presented correlation results provided a justification for the sample size used in the correlation analysis. There are a number of easily accessible tools that can be used to determine the required sample size for inference based on a Pearson correlation coefficient; however, we were unable to locate any widely available tools that can be used for sample size calculations for a Spearman correlation coefficient or a Kendall coefficient of concordance. In this article, we provide formulas and charts that can be used to determine the required sample size for inference based on either of these coefficients. Additional sample size charts are provided in the Supplementary Materials.
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