Which test measures the correlation between nominal data?

The Contingency coefficient is the appropriate test for measuring the correlation between nominal data. It is a statistic used to determine the strength of association between two categorical variables in a contingency table. When two variables are measured at a nominal level (for example, gender and preference for a type of product), the Contingency coefficient can help quantify how closely related the two variables are.

In practical terms, the Contingency coefficient calculates a value that ranges from 0 to 1, where 0 indicates no association and 1 indicates a perfect association. This makes it particularly useful in examining relationships within categorical data, which is essential in fields such as market research, social sciences, and epidemiology where nominal variables are often analyzed.

The other methods mentioned, while useful for different types of data or relationships, do not apply specifically to nominal data. Spearman's rank correlation and Kendall's tau both assess ordinal data by measuring the rank correlation, while Pearson correlation evaluates linear relationships between two continuous variables. Thus, the Contingency coefficient stands out as the correct measure for assessing correlation between nominal data.