Finding Multivariate Outliers with the Mahalanobis Distance Test in SPSS

When you are cleaning your raw data, you will want to check for outliers; particularly multivariate outliers, because they can really mess up your analysis. The Mahalanobis Distance test identifies outliers among multiple variables, called multivariate outliers. The biggest problem is that the Mahalanobis test is hidden in the Regression commands and not intuitive to find. I will show you how to compute Mahalanobis scores, sort them, check their probability using a Chi-square distribution table, and then a shortcut to let SPSS compute Mahalanobis probabilities. We begin with a review of outliers for those who want to brush up on the concepts, an explanation of how to choose cutoff scores for exploratory data analysis, and we wrap up with how to report the Mahalanobis findings in APA style. This is the full video about the Mahalanobis Distance test. I also have an abbreviated video that only covers how to do the test, create probabilities, and write up results. Short video: https://youtu.be/WvGq5LdmE4Y This video teaches the following commands and techniques: Multivariate outliers Linear Regression Mahalanobis test Compute Variable Chi square distribution table Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Boston, MA: Allyn and Bacon. SYNTAX: COMPUTE pMAH_1= 1 - CDF.CHISQ(mah_1,8). EXECUTE. SORT CASES BY pMAH_1 (A). This series uses the data set Mahalanobis_PH.sav Link to a Google Drive folder with all of the files that I use in the videos including the dataset and the Excel Chi-square distribution table https://drive.google.com/drive/folders/1n9aCsq5j4dQ6m_sv62ohDI69aol3rW6Q?usp=sharing