Which analysis method is suitable for measuring correlation in ordinal data?

Spearman's rank correlation is specifically designed for assessing the strength and direction of the association between two ordinal variables. It works by ranking the data and then calculating the correlation based on these ranks. This method is particularly beneficial when the data does not meet the assumptions required for Pearson’s correlation, which is designed for interval or ratio data and assumes a linear relationship.

Ordinal data, which consists of categorical values that have a meaningful order but no consistent difference between the categories, is best suited for Spearman's rank correlation because it preserves the rank order of the data without making any assumptions about the distribution or intervals between ranks. This adaptability to varying data types makes Spearman's rank correlation a robust choice for analyzing ordinal variables and understanding their relationships.

Contingency coefficients and Kendall's tau also serve the purpose of measuring relationships in categorical data, but they do so under different conditions and may not directly apply to all ordinal situations as effectively as Spearman's.