Shimizu, Y. (清水佑輔) (2022). 
Multiple desirable methods in outlier detection of univariate data with R source codes. 
Frontiers in Psychology, 12, 819854.

The existence of outliers has been a methodological obstacle in various literature. There are many cases when we should deal with outliers of univariate data. If inappropriate methods are used, it can lead to biased and wrong conclusions. The purpose of this opinion paper is reviewing more desirable methods for detecting outliers of univariate data (specifically, square root transformation, median absolute deviation, Grubbs’ test, and Ueda’s method), and presenting source code and sample data that allow us to conduct each detection method. These detection methods have desirable advantages over the conventional method and they are relatively easy to implement. In addition, the results of applying each outlier detection method to a real data set are shown. Presented methods in this article can be conducted using R, a free statistical software. By summarizing various outlier detection methods and providing analysis source codes, useful knowledge in psychological research can be provided.