Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median

TLDR: In this article, the authors highlight the disadvantages of this method and present the median absolute deviation, an alternative and more robust measure of dispersion that is easy to implement, and explain the procedures for calculating this indicator in SPSS and R software.

About: This article is published in Journal of Experimental Social Psychology.The article was published on 2013-07-01 and is currently open access. It has received 2647 citations till now. The article focuses on the topics: Robust measures of scale & Median absolute deviation.

Frequently asked questions

Q1. What is the purpose of this paper?

The aim of this paper is twofold: (a) showing that many researchers use a very poor method to detect outliers; (b) outlining the Median Absolute Deviation (MAD) method as a way of dealing with the problem of outliers. 

Q2. What is the breakdown point of the estimator?

The estimator's breakdown point is the maximum proportion of observations that can be contaminated (i.e., set to infinity) without forcing the estimator to result in a false value (infinite or null in the case of an estimator of scale). 

Q3. What is the procedure for calculating the MAD?

The procedure for calculating the MAD is simple, the authors have to: (a) compute the median using the menu “Analysis” and the command “Frequency”; (b) subtract this value from all observations in the statistical series using the command “Compute” in the menu “Transform”; (c) compute the median of the resulting new variable as in the first point, and (d) multiply this value by 1.4826 (if the authors assume normality ofthe data).