Pearson versus Spearman correlation
Only the Spearman correlation coefficient, which is usually not for yourself easily by seeing what the correlation coefficient is between x and. The difference between Pearson's and Spearman's correlation is that the Pearson is most appropriate for measurements taken from an interval scale. The Pearson correlation evaluates the linear relationship between two continuous variables. A relationship is linear when a change in one variable is associated.
Pearson product moment correlation The Pearson correlation evaluates the linear relationship between two continuous variables.
A relationship is linear when a change in one variable is associated with a proportional change in the other variable.
Pearson vs. Spearman Correlation Coefficients
For example, you might use a Pearson correlation to evaluate whether increases in temperature at your production facility are associated with decreasing thickness of your chocolate coating. Spearman rank-order correlation The Spearman correlation evaluates the monotonic relationship between two continuous or ordinal variables.
In a monotonic relationship, the variables tend to change together, but not necessarily at a constant rate. The Spearman correlation coefficient is based on the ranked values for each variable rather than the raw data.Spearman Correlation
Spearman correlation is often used to evaluate relationships involving ordinal variables. For example, you might use a Spearman correlation to evaluate whether the order in which employees complete a test exercise is related to the number of months they have been employed. When should you use the Spearman's rank-order correlation? The Spearman's rank-order correlation is the nonparametric version of the Pearson product-moment correlation. What are the assumptions of the test? You need two variables that are either ordinal, interval or ratio see our Types of Variable guide if you need clarification.
Although you would normally hope to use a Pearson product-moment correlation on interval or ratio data, the Spearman correlation can be used when the assumptions of the Pearson correlation are markedly violated. However, Spearman's correlation determines the strength and direction of the monotonic relationship between your two variables rather than the strength and direction of the linear relationship between your two variables, which is what Pearson's correlation determines.
Spearman's Rank-Order Correlation using SPSS Statistics
What is a monotonic relationship? Just remember that if you do not test these assumptions correctly, the results you get when running a Spearman's correlation might not be valid.
This is why we dedicate a number of sections of our enhanced Spearman's correlation guide to help you get this right.
You can find out about our enhanced content as a whole hereor more specifically, learn how we help with testing assumptions here. Spearman's correlation determines the degree to which a relationship is monotonic. Put another way, it determines whether there is a monotonic component of association between two continuous or ordinal variables.
As such, monotonicity is not actually an assumption of Spearman's correlation. However, you would not normally want to pursue a Spearman's correlation to determine the strength and direction of a monotonic relationship when you already know the relationship between your two variables is not monotonic.
Instead, the relationship between your two variables might be better described by another statistical measure of association.