Detecting Outliers in IBM SPSS Statistics

This guide provides detailed instructions for detecting outliers in IBM SPSS Statistics. It outlines the steps to open the software, load a dataset, and navigate to the appropriate analysis options. Users are instructed to select the variables for analysis and choose the desired statistics and plots before executing the procedure. The output section describes how to check descriptives, including the difference between the mean and the 5% trimmed mean, and emphasizes the importance of reviewing the table of outliers and visualizations such as boxplots and stem-and-leaf plots. Additionally, the guide presents methods for flagging outliers using data validation techniques. It advises on best practices for visualizing data, recognizing the impact of outliers, and documenting the approach to handling them. The document defines outliers and anomalous cases, stressing the importance of data understanding and examination in the analysis process.