Which of the following best describes Spearman's rho?

• A. It measures linear association for two interval-level variables
• B. It is the same as Pearson's r for all data
• C. It measures the strength and direction of a monotonic relationship using ranked data
• D. It requires both variables to be normally distributed

Answer: (C)

Spearman's rho is a nonparametric measure of rank correlation. It looks at how well the relationship between two variables can be described by a monotonic function, using the data ranks rather than the raw values. Because it uses ranks, it captures both the strength and the direction of association: a positive rho means the variables tend to move in the same direction (as one increases, the other tends to increase), while a negative rho means they move in opposite directions.

This approach is particularly useful when the relationship is monotonic but not necessarily linear, or when the data are ordinal or not normally distributed, since ranking reduces the impact of outliers and distributional assumptions. It’s not meant to measure linearity per se—that role belongs to Pearson’s correlation. In general, Spearman and Pearson correlations are not identical for all data, though they may be similar when the relationship is perfectly linear with no ties.