In conducting a chi-square test, how many variables does it analyze?

The chi-square test primarily analyzes two variables to determine if there is a significant association between them. Specifically, this test is often used to evaluate the relationship between categorical variables through contingency tables. In its basic form, it assesses whether the frequencies observed in each category correspond to the expected frequencies if no relationship exists between the variables.

The focus on two variables allows researchers to assess relationships such as those found in demographic studies, where one might investigate the association between gender and voting preference, for example. It is important to note that while the chi-square test can be applied in studies involving multiple groups across many categories, it fundamentally evaluates the interaction between pairs of categorical variables.

In contrast, analyzing only one variable relates more to descriptive statistics rather than hypothesis testing, and addressing more than two variables typically falls under different analytical techniques outside the scope of the chi-square test, such as logistic regression or multivariate analysis. Thus, identifying that the chi-square test analyzes two variables is essential for correctly applying the method in research.