Analyze data using Spearman’s rank correlation coefficient (rho)
- It measures the relationship between two logically related variables. Like the conventional correlation coefficient (r), Spearman rho can have any value between -1 to +1. A Spearman correlation of 1 results when the two variables being compared are monotonically related, even if their relationship is not linear. In a monotonic relationship, the variables tend to move in the same relative direction, but not necessarily at a constant rate. Monotonic relationship can be nonlinear. In a linear relationship, the variables move in the same direction at a constant rate.
CLICK HERE to refer to the diagrams of Linear, Nonlinear and monotonic relationships.
In Statistics, Correlation is largely a measure of an association between variables. In logically correlated data, the change in the magnitude of 1 variable is related to a corresponding change in the magnitude of another variable, either in the same (positive correlation: High-High, Low-Low) or in the opposite (negative correlation: Low-High or High-Low) direction. Very often, the term correlation is used in the context of a linear relationship between 2 continuous variables and expressed as Pearson product-moment correlation. For monotonically (nonlinear) distributed continuous ordinal data(indicate the order or rank of things) or for data with relevant outliers,  Spearman rank correlation can be used as a measure. Spearman rho refers to the ranked values rather than the original measurements.
