By Contributor, Updated Aug 30, 2022

Box plots, stem‑and‑leaf plots, and normal Q‑Q plots are essential exploratory tools that let you visualize data distributions and identify outliers before conducting statistical tests. SPSS (formerly PASW Statistics) can generate all three plots in a few clicks, providing a quick visual check of your data’s shape and quality.

Step 1

Open your dataset in SPSS. From the Analyze menu, choose Descriptive Statistics > Explore.

Step 2

Select the variables you want to examine and use the left‑pointing arrow to move them into the Dependent box at the top right of the dialog.

Step 3

Click OK. SPSS will produce:

• A box plot that shows the median, quartiles, and potential outliers.
• A stem‑and‑leaf plot that displays the raw data distribution.
• Two Q‑Q plots – one detrended and one raw – to assess normality.
• A descriptive statistics table that includes metrics not available in the basic Descriptives window, such as interquartile range, 5 % trimmed mean, and a 95 % confidence interval for the mean.

TL;DR

The Extreme Values table lists the highest and lowest cases for each variable, enabling you to quickly spot values that may be due to measurement error or other anomalies.

By following these steps, you can efficiently assess your data’s distribution, detect outliers, and ensure the integrity of subsequent analyses.