The Rank Difference method of computing the coefficient of correlation, also known as Spearman's rank correlation coefficient, is a non-parametric measure of statistical dependence between two variables. It assesses how well the relationship between two variables can be described using a monotonic function.

Spearman's rank correlation coefficient ranges from -1 to +1. A value of +1 indicates a perfect positive correlation, while a value of -1 indicates a perfect negative correlation. A value of 0 indicates no correlation.

This method is particularly useful when the data is not normally distributed or when the relationship between variables is not linear. It also helps to mitigate the effects of outliers.

Use Spearman rank correlation when you have two ranked variables, and you want to see whether the two variables covary; whether, as one variable increases, the other variable tends to increase or decrease. You also use Spearman rank correlation if you have one measurement variable and one ranked variable; in this case, you convert the measurement variable to ranks and use Spearman rank correlation on the two sets of ranks.

https://stats.libretexts.org/Bookshelves/Applied_Statistics/Biological_Statistics_(McDonald)/05%3A_Tests_for_Multiple_Measurement_Variables/5.02%3A_Spearman_Rank_Correlation