Can Spearman's correlation be used when data is at the ordinal level?

Spearman's correlation is indeed suitable for use with ordinal-level data. This statistical method is a non-parametric measure that evaluates the strength and direction of association between two ranked variables. Because it operates on the ranks rather than the actual data values, it effectively handles situations where the assumptions of parametric tests—such as normality and homoscedasticity—are not met, which is often the case with ordinal data.

Ordinal data consists of categories with a meaningful order, but the intervals between these categories may not be equal or consistent. Spearman's correlation does not require the data to be interval or ratio level; it simply requires that the data can be ranked. By transforming the data into ranks, Spearman’s correlation allows for the examination of relationships in datasets that might not meet the strict requirements of Pearson’s correlation, which is applicable only for interval or ratio data.

Overall, the ability of Spearman's correlation to effectively analyze ordinal data makes it a valuable tool in various research settings, particularly in the fields of psychology and social sciences, where ordinal scales are commonplace.