In statistics, Spearman's rank correlation coefficient or Spearman's ρ is a number ranging from -1 to 1 that indicates how strongly two sets of ranks are correlated. It could be used in a situation where one only has ranked data, such as a tally of gold, silver, and bronze medals.
Spearman’s correlation in statistics is a nonparametric alternative to Pearson’s correlation. Use Spearman’s correlation for data that follow curvilinear, monotonic relationships and for ordinal data.
What is Spearman Rank Correlation / Spearman’s Rho? The Spearman rank correlation coefficient, r s, is the nonparametric version of the Pearson correlation coefficient.
Spearman’s Rank Correlation is a statistical measure used to find the strength and direction of association between two ranked variables. It checks how well the relationship between two variables can be described using a monotonic function.
This guide will help you understand the Spearman Rank-Order Correlation, when to use the test and what the assumptions are. Page 2 works through an example and how to interpret the output.
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.
When data is not normally distributed or when the presence of outliers gives a distorted picture of the association between two random variables, Spearman’s rank correlation is a non-parametric test that can be used instead of the Pearson’s correlation coefficient.
The Spearman rank correlation coefficient, also known as Spearman's rho, is a nonparametric (distribution-free) rank statistic proposed by Spearman in 1904 as a measure of the strength of the associations between two variables (Lehmann and D'Abrera 1998).