Spearman's rank correlation coefficient

The Spearman rank correlation examines the relationship between two variables. Spearman's rank correlation is the non-parametric counterpart of Pearson's correlation. Therefore, normal distribution of the data is not required for Spearman correlation.

There is an important difference between the two correlation coefficients! Spearman correlation uses the ranks of the data rather than the original data, hence the name rank correlation.

Example of Spearman correlation

We measured the reaction time of 8 computer gamers and asked them about their age.

If we use a Pearson correlation, we simply take the two variables reaction time and age and calculate the Pearson correlation coefficient. However, we now want to calculate the Spearman rank correlation, so we first assign a rank to each person for reaction time and age.

The reaction time is already sorted by size. 12 is the smallest value, so it gets rank 1, 15 is the second smallest, so it gets rank 2 and so on. Now we do the same with the age.

Let's take a look at this in a scatter plot. On the left side we see the initial data of age and responsiveness and on the right side the ranks.

We have studied 8 people and since we have no rank correlations, we therefore have 8 ranks to assign. With this transformation we now have a more even distribution of the data.

To calculate the Spearman correlation, we simply calculate the Pearson correlation of the ranks. So the Spearman correlation is the same as the Pearson correlation, except that the ranks are used instead of the original values.

Let's have a quick look at this in DATAtab. You can load the data we used here.

Once we have the reaction time and age and then we have the just created ranks from the reaction time and age.

Now we can either calculate the Spearman rank correlation from the reaction time and age or we can calculate the Pearson correlation from the ranks. In both cases we get a correlation of 0.9.

Spearman Correlation Equation

If there are no rank ties, this equation can also be used to calculate the Spearman correlation.

Where n is the number of cases and d is the difference between the rankings of the two variables. For our example, the result is as follows:

The sum of d_i² is 8 and n, which is the number of people, is also 8. If we put it all in, we get a correlation coefficient of 0.9.

Spearman correlation coefficient

Like Pearson's correlation coefficient r, the Spearman's correlation coefficient r_s also varies between -1 and 1.

Using the coefficient, we can now determine two things:

• How strong the correlation is
• and the direction of the correlation.

The strength of the correlation can be read from a table.

If we have a coefficient between -1 and less than 0, there is a negative correlation, that is, a negative relationship between the variables. If we have a coefficient greater than 0 and greater than 1, there is a positive correlation, that is, a positive relationship between the two variables. If the result is 0, there is no correlation.

Testing the significance of correlation coefficients

However, often our aim is to test a hypothesis about the population from a sample.

We have calculated the correlation coefficient for the sample data. We can now test whether the correlation coefficient is significantly different from 0.

The null hypothesis and the alternative hypothesis are as follows:

• Null hypothesis: the correlation coefficient r = 0 (There is no correlation).
• Alternative hypothesis The correlation coefficient r ≠ 0 (There is a correlation).

Whether the correlation coefficient is significantly different from zero, based on the sample collected, can be tested using a t-test.

Where r is the correlation coefficient and n is the sample size. A p-value can then be calculated from the test statistic t. If the p-value is less than the specified significance level (usually 5%), then the null hypothesis is rejected, otherwise it is not.

If we use DATAtab to calculate the example, we get a p-value of 0.002.

Therefore, the p-value is less than 0.05 and we can reject the null hypothesis that the correlation coefficient is zero in the population.