When is the Spearman correlation coefficient appropriate to use?

The Spearman correlation coefficient is appropriate to use when one variable is ordinal and the other is interval. This is because the Spearman correlation measures the strength and direction of the association between two ranked variables. It does not assume a normal distribution of the data or the linear relationship that is required by the Pearson correlation coefficient.

In situations where you have an ordinal variable, which consists of ranked categories (such as a satisfaction scale), and an interval variable, which is measured on a scale that has equal distances between values (like temperature), the Spearman correlation can effectively assess how these two types of data move together.

When both variables are normally distributed, it is more appropriate to use the Pearson correlation coefficient, which is designed for continuous and normally distributed data. If there is only one variable to examine, correlation analysis is unnecessary, as correlation requires at least two variables. Lastly, the Spearman correlation can still be used when the two variables do not have a linear relationship, making it versatile, but its primary design is to deal with ranked data when at least one variable is ordinal. Thus, the choice of using ordinal and interval data together is most aligned with Spearman’s intended application.