**Instructions:** You can use this Spearman Correlation Calculator tool to compute Spearman's Correlation Coefficient for two variables X and Y. All you have to do is type your X and Y data, either in comma or space separated format (For example: "2, 3, 4, 5", or "3 4 5 6 7").

## More about the Spearman correlation coefficient

The correlation coefficient calculated above corresponds to Spearman's correlation coefficient. The requirements for computing it is that the two variables X and Y are measured at least at the interval level (which means that it does not work with nominal or ordinal variables).

The formula for Pearson's correlation coefficient is:

\[r_S =\frac{n \sum_{i=1}^n Rank(x_i) Rank(y_i) - \left(\sum_{i=1}^n Rank(x_i) \right) \left(\sum_{i=1}^n Rank(y_i) \right) }{\sqrt{n \sum_{i=1}^n Rank(x_i)^2 - \left( \sum_{i=1}^n Rank(x_i) \right)^2} \sqrt{n \sum_{i=1}^n Rank(y_i)^2 - \left( \sum_{i=1}^n Rank(y_i) \right)^2} }\]or equivalently

\[r_S = \frac{SS_{\tilde X \tilde Y}}{\sqrt{SS_{\tilde X \tilde X}\cdot SS_{\tilde Y \tilde Y} }}\]For interval level data, you should use Pearson's correlation coefficient calculator. Also, to graphically visualize the data and understand better the linear association between the variables X and Y, you can use our scatterplot maker

How to report Spearman correlation? Very similarly to the way it is reported for the case of Pearson's correlation. Typically you will write something like: "The ordinal variables X and Y show a significant degree of linear association, \(r_s = .894, p < .001\)."

In case you have any suggestion, or if you would like to report a broken solver/calculator, please do not hesitate to **contact us**.