Describe Spearman's Rank Correlation

Spearman's Rank Correlation is used to measure the degree of correlation between two variables, such as by ranking them in order of magnitude. Specifically, it measures the degree of monotonic relationship between the two variables. Monotonic means that as the numerical value of one of the variables increases, the corresponding value of the other variable either increases or decreases. The correlation coefficient that is derived from Spearman's Rank Correlation ranges from +1 to -1. A +1 indicates a perfect monotonic relationship (all items on one variable increase when the same items on the other variable increase), a -1 indicated a perfect inverse monotonic relationship (all items on one variable increase when the same items on the other variable decrease). A correlation coefficient of 0 indicates no monotonic relationship across the variables. Spearman's Rank Correlation is frequently used when items cannot be measured in a numerical manner, such as in Likert-type surveys, or if a presumed linear relationship does not exist between variables. Unlike the Pearson Correlation, which assumes that all raw values for each variable fall on a linear relationship, the Spearman correlation can accurately measure the strength of relationship based on only the magnitude of the observed values.