Key Takeaways:

- Spearman Correlation in SPSS is an alternative to Pearson Correlation.
- Data must be ranked/ordinal.
- Results vary from no correlation (0) to perfect correlation (±1).

**Spearman correlation** is an alternative to Pearson correlation for ranked data or data measured on the ordinal scale. **Note**: If you are unfamiliar with how SPSS categorizes variables, see: SPSS Nomimal Ordinal Scale.

For this example, I’m using one of the **SPSS sample datasets,** which come with every download of SPSS 28. If you want to follow along with the example, first open the “breakfast” file. To find the file:

- Click File → Open → Data.
- Click C: → rRogram Files → IBM → SPSS Statistics → Samples.
- Select your language and then click the file name “breakfast”.

## Spearman Correlation in SPSS: Steps

For this example, I’m going to use the “breakfast” dataset to see if there is any correlation for preferences of English muffins and Jelly Donuts.

Step 1: **Check to make sure your variables are ranked or measured on the ordinal scale. **In the breakfast sample dataset, both of my variables are on the ordinal scale.

Step 2: **Click Analyze → Correlate → Bivariate.** This action opens the Bivariate correlation window.

Step 3: **Move the variables of interest over to the Variables box. **To do this, highlight each variable then click the blue arrow in the center.

Step 4: **Uncheck the Pearson correlation check box and then place a check the Spearman correlation check box.**

When you have completed the action, your window should look like this:

Step 5: **Click OK.**

Step 6: **Read the results **in the output viewer window (you may need to scroll down).

Note that the SPSS output viewer will give you correlations for all variables, including correlations of a variable (e.g., English muffin) with itself (this gives a perfect correlation of 1). But the result we are interested in here is the intersection of jelly donuts and English muffin (highlighted in green). This shows a moderate negative correlation of .430. The p-value (in red) is tiny (.001), indicating this is a statistically significant result.