# How to Calculate Pearson's R (Pearson Correlations) in SPSS / PASW Statistics

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In statistics, the Pearson R correlation illustrates the association between two variables that are on an interval or ratio scale. While you can calculate the Pearson's R by hand, the statistical software suite known as SPSS -- now also known as PASW Statistics -- makes it easy.

### Things You'll Need

• SPSS (aka PASW Statistics 17), any version
• Input data into the SPSS program to calculate a Pearson correlation between two variables. You'll need at least two numeric variables for correlation.

• Go to "Analyze" menu, select "Correlate -> Bivariate..." When presented with a screen with two boxes, click on a variable in the box on the left, and the arrow between the boxes to move the variable over to the box on the right. Repeat this process for at least one additional variable. The "Variables" box on the right should now contain at least two items.

• Select either a one- or two-tailed test from the option buttons at the bottom of the window. Recall that directional hypotheses call for one-tailed tests, while nondirectional hypotheses call for two-tailed tests. If the significance level of your correlation is not important to you, don't worry about it -- the value of your final R statistic will be the same either way.

## Tips & Warnings

• If you wish to see additional statistics, then before clicking "OK", click the "Options..." button at the top left hand corner of the window. You'll be presented with options to view additional descriptives such as means, standard deviations, cross-product deviations and covariance statistics.
• The "Options" button also allows you to exclude cases "pairwise," which means any pair of data points being compared will be excluded if one of these points is missing, or "listwise," which means the entire row will be excluded. There's not much difference between these options if you are only correlating two variables. If you are comparing three or more variables at once, however, excluding cases pairwise retains more data.

## References

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