Mowbray Fabrice I, Fox-Wasylyshyn Susan M, El-Masri Maher M
1 Faculty of Nursing, University of Windsor, Windsor, Ontario, Canada.
Can J Nurs Res. 2019 Mar;51(1):31-37. doi: 10.1177/0844562118786647. Epub 2018 Jul 3.
The presence of statistical outliers is a shared concern in research. If ignored or improperly handled, outliers have the potential to distort the estimate of the parameter of interest and thus compromise the generalizability of research findings. A variety of statistical techniques are available to assist researchers with the identification and management of outlier cases. The purpose of this paper is to provide a conceptual overview of univariate outliers with special focus on common techniques used to detect and manage univariate outliers. Specifically, this paper discusses the use of histograms, boxplots, interquartile range, and z-score analysis as common univariate outlier identification techniques. The paper also discusses the outlier management techniques of deletion, substitution, and transformation.
统计异常值的存在是研究中一个共同关注的问题。如果被忽视或处理不当,异常值有可能扭曲感兴趣参数的估计,从而损害研究结果的可推广性。有多种统计技术可帮助研究人员识别和处理异常值情况。本文的目的是提供单变量异常值的概念性概述,特别关注用于检测和处理单变量异常值的常用技术。具体而言,本文讨论了使用直方图、箱线图、四分位距和z分数分析作为常见的单变量异常值识别技术。本文还讨论了删除、替换和变换等异常值处理技术。