# How to Create a Scatter Plot in SPSS

Scatter plots allow us to visualize the relationship between two numeric variables.  This quick tutorial will show you the easiest method to create a simple scatter plot in SPSS.

### Quick Steps

1. Click Graphs -> Legacy Dialogs -> Scatter/Dot (in older versions of SPSS) OR
Click Graphs > Scatter/Dot (in newer versions of SPSS)
2. Select Simple Scatter
3. Click Define
4. Click Reset (recommended)
5. Select the predictor/independent variable and move it into the X Axis box
6. Select the criterion/dependent variable and move it into the Y Axis box
7. Select Titles to add a title (recommended), then click Continue
8. Select OK
9. Your scatter plot will appear in SPSS Output Viewer
Adding a Line of Best Fit (Optional)
1. Double-click on your scatter plot to open the Chart Editor
2. Click Elements -> Fit Line at Total
3. Ensure that Linear is selected under Fit Method
4. Click Close
5. Click X in the top right corner of the Chart Editor to save your edits.

## The Data

The starting assumption for this tutorial is that you have already imported your data into SPSS, and that you’re looking at something like the data set below. (Check out our tutorials on importing data from Excel or MySQL into SPSS).

This hypothetical data set contains the mid-term and final exam scores of 40 students in a Statistics course (the first 20 records are displayed above).  We want to create a scatter plot to visualize the relationship between the two sets of scores.

## Create a Scatter Plot

Click Graphs -> Legacy Dialogs -> Scatter/Dot as illustrated below.  Note, however that in newer versions of SPSS, you will need to click GraphsScatter/Dot.

This brings up the Scatter/Dot dialog box:

Select Simple Scatter and then click Define.

This brings up the “Simple Scatterplot” dialog box below.

We recommend that you click the Reset button to clear any previous settings.

The next step is to move your variables into the X Axis and Y Axis boxes.  If your data is from a regression study, select your predictor/independent variable, and use the arrow button to move it to the X Axis.  Then select the criterion/dependent variable, and use the arrow button to move it to the Y Axis box.  If your data is from a simple correlation study, as is the case with our example, there may not be obvious predictor/independent and criterion/dependent variables.  In these cases, it doesn’t matter which variable you move to the X Axis box and which variable you move to the Y Axis box.

It is a good idea to give your scatter plot a title.  To do this, click the Titles button, add your title, and click Continue to return to the “Simple Scatterplot” dialog box.

Select OK to generate your scatter plot.

## The Scatter Plot

The SPSS Output Viewer will pop up with the scatter plot that you’ve created.

Each student in our hypothetical study is represented by one dot on our scatter plot.  Each dot’s position on the X (horizontal) axis represents a student’s mid-term exam score, and its position on the Y (vertical) axis represents their final exam score.

After we create a scatter plot, we need to review it to assess the nature of the relationship – if any – that exists between our variables.  The scatter plot above indicates that there is a positive linear relationship between mid-term and final exam scores in this Statistics course.  In other words, lower mid-term exam scores tend to be associated with lower final exam scores, and higher mid-term exam scores tend to be associated with higher final exam scores.  It is important to note that a scatter plot cannot prove a causal relationship between variables.  Therefore, we cannot conclude that high mid-term exam scores cause high final exam scores on the basis of the scatter plot above.

Some of the other relationships between variables that your scatter plot may indicate are illustrated below.

 Negative Linear Relationship Between Variables Curvilinear Relationship Between Variables Positive Monotonic Relationship Between Variables.As the value of one variable increases, the value of the other value also increases. However, the relationship between these variables is not linear. Negative Monotonic Relationship Between Variables.As the value of one variable increases, the value of the other value decreases. However, the relationship between these variables is not linear. No Clear Relationship Between Variables