The chi-square test of association is used to determine if there is an association between two categorical variables.
In statistics, we call "dependent variable" a variable that is supposed to depend on the values of other variables, which are called "independent variables".
In regression, it is really clear which variable is dependent (the y that we are trying to predict) and which ones are independent (the xs predictors)
However it is not at all clear to me what are the "independent" and "dependent" variables in a chi-square test of association between variable A and B.
The options I see are:
- Both A and B are dependent variable; there is no independent variable
- Either A is dependent and B is independent, or A is independent and B is dependent. The variable that is hypothesized to cause the other is the independent variable, and the other is the dependent variable. If we don't know which one causes which, we pick randomly.
- Both A and B are independent variables; the dependent variable is actually the frequency (count) of each combination of A & B.
- The independent / dependent variable framework is inappropriate for chi-square tests of association
I read some support for all of these positions, so I'm confused as to which interpretation is preferable.