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Statistics Resources

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Spearman's

The Spearman Correlation is the nonparametric equivalent of the Pearson correlation and is appropriate when the relationship between variables is not linear and/or when the variables are of an ordinal level of measurement. This approach can also be used when the data is not normally distributed and is not sensitive to outliers, unlike the Pearson correlation.

Assumptions

  1. Paired observations - each pair of values is contributed from a single participant
  2. Monotonic relationship - assessed through a visual examination of the scatterplot

Running Spearman Correlation in SPSS

  1. Analyze > Correlate > Bivariate
  2. Move variables of interest to the "Variables" box.
  3. Select "Spearman" as the test. (You may need to uncheck "Pearson" as well)
  4. You may use the "Options" button to select descriptive statistics you wish to include as well.
  5. Click "OK" to run the test.

Interpreting the Output

The results will generate in a matrix. You can ignore any boxes that show a "1" as the correlation value as these are simply the variable correlated with itself. These values will form a diagonal across the matrix that can be used to help you focus on the correct values. You only need to explore the correlation values on half of the matrix. APA Style uses the bottom half.

Results Table showing Correlation Findings

With the release of SPSS 27, users now have the option to only produce the lower half of the table, which is in line with APA Style and makes it easier to identify the correct correlation values.

Depictions of SPSS output

Reporting Results

When reporting the results of the correlation analysis, APA Style has very specific requirements on what information should be included. Below is the key information required for reporting the Spearman Correlation results. You want to replace the red text with the appropriate values from your output.

rs(degrees of freedom) = the rs statisticp = p-value

Example:

A Spearman's rank-order correlation was run to determine the relationship between 10 students' French and Chemistry final exam scores. There was a strong, positive correlation between these scores, which was statistically significant (r(8) = .669, p = .035).

Notes:

  • When reporting the p-value, there are two ways to approach it. One is when the results are not significant. In that case, you want to report the p-value exactly: p = .24. The other is when the results are significant. In this case, you can report the p-value as being less than the level of significance: p < .05.
  • The r statistic should be reported to two decimal places without a 0 before the decimal point: .36
  • Degrees of freedom for this test are N - 2, where "N" represents the number of people in the sample. N can be found in the correlation output.

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