I'm currently analyzing a data set from an experiment, where participants could give one of three types of answers: 1) Correct, 2) incorrect-congruent, 3) incorrect-incongruent. The priming participants where exposed to beforehand also varied along these lines, being either a) congruent, b) incongruent or c) no priming.
My hypothesis is now, that congruent priming increases congruent answers, but that incongruent priming will not increase incongruent answers. This should show in an interaction between prime type (a and b) and answer type. Note that answers are not independent from each other (if the answer is type a, it cannot simultaneously be type b or c).
According to my research, the appropriate analysis would be a Poisson Regression, but my mentor suggested a Log-Linear Model. When looking this up on the internet, it seems that these analyses are sometimes used interchangeably.
My Question(s): Are they the same? If not, do they apply to the same data? And then, which one would be the correct analysis for my current data set?
Best Answer
Log-linear Poisson regression (with interactions) on the counts in a higher dimensional frequency table is a standard way to analyze associations between categorical variables. So in your setting, the two terms are interchangable.
In your two-dimensional setting, you can also use less general methods (Cramér's V, percentages, chisquared-test; depending on what you are interested).