When should Spearman rank-order correlation be used instead of Pearson’s correlation coefficient?

The choice between Spearman rank-order correlation and Pearson’s correlation coefficient depends primarily on the nature of your data and the type of relationship you are investigating.

When to Use Spearman Rank-Order Correlation

The Spearman rank-order correlation coefficient (Spearman rho) should be used in the following situations:

• Ordinal or Ranked Data: It is designed for variables measured on an ordinal or ranked scale, where the data represent a hierarchy rather than precise interval or ratio values.1
• Non-Interval Scales: It is appropriate for data that are not truly interval in nature, such as responses from a Likert-scale survey.1
• Monotonic Relationships: It measures the strength of a monotonic association—meaning the variables move in a constant direction—rather than strictly linear relationships.1
• Data Transformation: It can be used when interval data are transformed into ranks, which may be necessary if one variable is measured on an interval scale and the other on an ordinal scale.1

Comparison with Pearson’s Correlation

In contrast, Pearson’s correlation coefficient is specifically used to quantify the strength of a linear relationship between two variables measured on a continuous (interval/ratio) scale. While Pearson’s is often preferred for its precision when assumptions are met, Spearman is a nonparametric method that provides a robust alternative when data do not meet the requirements for parametric analysis.213