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.

# Example dataset

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 …**’.

2. Next, select the ‘**Correlation**‘ test option, under the ‘**XY analyses**‘ header. Then click the ‘**OK**‘ button.

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.

# Output

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.

# Interpretation

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.

# Reporting

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