The Spearman rank-order correlation coefficient (Spearman’s correlation, for short), is a non-parametric measure of the strength and direction of association that exists between two variables measured on at least an ordinal scale. It is denoted by the symbol r_{s} (or the Greek letter , pronounced rho). The test is used for either ordinal variables or for interval data that has failed the assumptions necessary for conducting the Pearson's product-moment correlation. For example, you could use a Spearman’s correlation to understand whether there is an association between exam performance and time spent revising; whether there is an association between depression and length of unemployment; and so forth. If you would like some more background on this test, you can find it here.
This "quick start" guide shows you how to carry out a Spearman’s correlation using SPSS. We show you the main procedure for doing this here. However, first we introduce you to the assumptions that you must consider when carrying out Spearman’s correlation.
When you choose to analyse your data using Spearman’s correlation, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using a Spearman’s correlation. You need to do this because it is only appropriate to use a Spearman’s correlation if your data "passes" two assumptions that are required for Spearman’s correlation to give you a valid result. In practice, checking for these two assumptions just adds a little bit more time to your analysis, requiring you to click of few more buttons in SPSS when performing your analysis, as well as think a little bit more about your data, but it is not a difficult task. These two assumptions are:
In terms of assumption #2 above, you can check this using SPSS. If your data fails this assumption, you should consider using a different statistical test, which we show you how to do in our Statistical Test Selector (N.B., this is part of our enhanced content).
It is also worth noting that a Spearman’s correlation can be used when your two variables are not normally distributed. It is also not very sensitive to outliers. These are single data points within your data that do not follow the usual pattern (e.g., in a study of 100 students’ IQ scores, where the mean score was 108 with only a small variation between students, one student had a score of 156, which is very unusual, and may even put her in the top 1% of IQ scores globally). Since Spearman’s correlation is not very sensitive to outliers, this means that you can still obtain a valid result from using this test when you have outliers in your data.
In the section, Procedure, we illustrate the SPSS procedure to perform a Spearman’s correlation assuming that no assumptions have been violated. First, we set out the example we use to explain the Spearman’s correlation procedure in SPSS.
A teacher is interested in whether those who do better at English also do better in Maths. To test whether this is the case, the teacher records the scores of her 10 students in their end-of-year examinations for both English and Maths. Therefore, one variable is the English scores and the second variable is the Maths scores.
In SPSS, we created two variables so that we could enter our data: English_Mark (i.e., English scores) and Maths_Mark (i.e., Maths scores). In our enhanced Spearman's correlation guide, we show you how to correctly enter data in SPSS to run a Spearman's correlation. You can learn about our enhanced data setup content here. Alternately, we have a generic, "quick start" guide to show you how to enter data into SPSS, available here.
The four steps below show you how to analyse your data using Spearman’s correlation in SPSS when neither of the two assumptions in the previous section, Assumptions, have been violated. At the end of these four steps, we show you how to interpret the results from this test. If you are looking for help to make sure your data meets assumption #2, which is required when using Spearman’s correlations, and can be tested using SPSS, you can learn more about our enhanced guides here.
Click Analyze > Correlate > Bivariate... on the menu system as shown below:
Published with written permission from SPSS Inc., an IBM Company.
Transfer the variables English_Mark and Maths_Mark into the Variables box by dragging-and-dropping or by clicking the button. You will end up with a screen similar to the one below:
Published with written permission from SPSS Inc., an IBM Company.
Make sure that you uncheck the Pearson tickbox (it is selected by default in SPSS) and check the Spearman tickbox under the -Correlation Coefficients- area.
SPSS generates one main table for the Spearman’s correlation procedure that you ran in the previous section. If your data passed assumptions #2 (i.e., there is a monotonic relationship between your two variables), which we explained earlier in the Assumptions section, you will only need to interpret this one table. However, since you should have tested your data for this assumption, you will also need to interpret the SPSS output that was produced when you tested for it (i.e., your scatterplot results). If you do not know how to do this, we show you in our enhanced Spearman’s correlation guide. If you have tested your data for these assumptions, we provide a complete explanation of the output you will have to interpret in our enhanced Spearman’s guide. Remember that if your data failed this assumption, the output that you get from the Spearman’s correlation procedure (i.e., the table we discuss below), will no longer be correct.
However, in this "quick start" guide, we focus on the results from the Spearman’s correlation procedure only, assuming that your data met this assumption. Therefore, when running the Spearman’s correlation procedure, you will be presented with the table below, entitled, Correlations:
Published with written permission from SPSS Inc., an IBM Company.
The results are presented in a matrix such that, as can be seen, the correlations are replicated. Nevertheless, the table presents Spearman's correlation, its significance value and the sample size that the calculation was based on. In this example, we can see that Spearman's correlation coefficient, r_{s}, is 0.669, and that this is statistically significant (p = 0.035).
In our example, you might present the results are follows:
A Spearman's Rank Order correlation was run to determine the relationship between 10 students' English and maths exam marks. There was a strong, positive correlation between English and maths marks, which was statistically significant (r_{s}(8) = .669, p = .035).
In our enhanced Spearman’s correlation guide, we also show you how to write up the results from your assumptions test and Spearman’s correlation output if you need to report this in a dissertation/thesis, assignment or research report. We do this using the Harvard and APA styles. You can learn more about our enhanced content here.