Which variables are best for cross-tabulation?

Which variables are best for cross-tabulation?

• A. Two categorical variables.
• B. Two continuous variables.
• C. One categorical and one continuous.
• D. Qualitative descriptors only.

Answer: (A)

Cross-tabulation examines the joint distribution of two categorical variables by organizing observed frequencies into a contingency table. This setup works best when both variables are categorical because each axis represents distinct, finite categories and every cell shows how many observations fall into that category pair. With two continuous variables, there isn’t a natural grid of discrete categories to fill; you’d typically use scatterplots, correlation, or regression to understand their relationship. When one variable is categorical and the other continuous, you can compare means or distributions across categories, but that isn’t a traditional cross-tabulation of two variables in their original forms. If you’re limited to qualitative descriptors only, you can still cross-tabulate, but the clear, interpretable joint distribution comes from two categorical variables.

Cross-tabulation examines the joint distribution of two categorical variables by organizing observed frequencies into a contingency table. This setup works best when both variables are categorical because each axis represents distinct, finite categories and every cell shows how many observations fall into that category pair. With two continuous variables, there isn’t a natural grid of discrete categories to fill; you’d typically use scatterplots, correlation, or regression to understand their relationship. When one variable is categorical and the other continuous, you can compare means or distributions across categories, but that isn’t a traditional cross-tabulation of two variables in their original forms. If you’re limited to qualitative descriptors only, you can still cross-tabulate, but the clear, interpretable joint distribution comes from two categorical variables.