Which statistical method is specifically designed to assess the relationship between categorical variables?

The chi-square test is specifically designed to evaluate the relationship between categorical variables. This test determines whether there is a significant association between two categorical variables by comparing the observed frequencies of occurrences in each category to the frequencies that would be expected if there were no relationship between the variables.

For example, if researchers are interested in understanding whether gender is related to preference for a particular product (both are categorical variables), the chi-square test can provide insight into whether the distribution of preferences varies by gender. It utilizes a contingency table to summarize the data and assesses how the observed counts differ from what would be expected under the assumption of independence.

Other statistical methods mentioned, such as regression analysis, ANOVA, and correlation analysis, primarily deal with continuous variables or relationships between a continuous variable and categorical groupings but do not specifically focus on relationships exclusively within categorical variables as the chi-square test does. Thus, the chi-square test is uniquely suited for analyzing the interdependence or association in categorical data.