In this article, I will show you how to perform a Spearman rank correlation test by using GraphPad Prism. I will also show you how to interpret and report the results.
Assumptions of the Spearman rank correlation test
Before running the test, there are just 2 assumptions that the data has to pass. These are presented below.
- The two variables should be measured on either an ordinal, interval or ratio scale.
- There should be a linear relationship between the two variables. This can be tested by plotting the variables on a scatterplot.
Setting up the GraphPad sheet
To begin with, set-up the GraphPad sheet so that an ‘XY‘ table and graph type is selected. Then, select the ‘Enter and plot a single Y value for each point‘ option. Finally, select the ‘Create‘ button to open up the new sheet.
In this example, I have measured the height and body mass index (BMI) of 20 individuals. Thus, I want to see the strength of the correlation between the two variables by performing a Spearman rank correlation test.
Note that each individuals data is paired on a separate row so that their height and BMI are adjacent to each other.
The null hypothesis for this example will be:
“There is no association between the height and BMI of the individuals”.
And the alternative hypothesis will be:
“There is an association between the height and BMI of the individuals”.
Performing the test
- To perform a Spearman rank correlation test in GraphPad Prism, firstly, go to ‘Insert > New Analysis …’.
3. In the next window, click the ‘No. Compute nonparametric Spearman correlation‘ option under the ‘Assume data are sampled from a Gaussian distribution?‘ header. This will ensure a Spearman correlation test is performed, as opposed to a Pearson correlation test. Then click the ‘OK‘ button to run the test.
The output for the Spearman correlation test in GraphPad is rather simple. Each output is broken down below.
- r – The Spearman correlation coefficient value (rho). These range from -1 (a perfectly negative association), to 0 (no association), to 1 (a perfectly positive association).
- 95% confidence interval – The 95% confidence intervals for the r value.
- P (two-tailed) – The all-important P value for the test.
- P value summary – Denotes the strength of the P value. The ‘ns’ just means not significant.
- Exact or approximate P value? – Whether the P value is exact or approximate.
- Significant? (alpha = 0.05) – A simple ‘Yes’ or ‘No’ output for the overall significance of the test.
- Number of XY Pairs – The number of data pairs in the test.
Simply looking at the ‘Significant? (alpha = 0.05)‘ output, it can be seen that a ‘No‘ is given. Thus, the test was not significant. This is also seen in the actual P value, which was 0.7345.
Therefore, the null hypothesis is accepted and the alternative hypothesis rejected.
To report the results of a Spearman correlation test, it is best to include the correlation coefficient value to indicate the strength of the relationship between the two values, as well as the P value. I have included an example of the reporting from the example used here.
GraphPad Prism version used: 6