I conducted a study including two CBC exercises. Participants were randomly assigned to either perform CBC exercise 1 or 2, so I have two treatment groups with different respondents.
The CBC exercises were the same in terms of attributes and attribute levels and only differed in terms of the described CBC starting scenario. (Background info: My CBC exercise(s) included 5 attributes, 2 of which have 3 levels, and the remaining 3 attributes have 2 levels respectively. There are no interaction effects I need to include in HB estimation)
Now I want to examine, how the starting scenario affects the results of the CBC and thus I want to compare the results of the two treatment groups. I use HB estimation. (I ruled out alternative explanations for group differences such as differences in demographics)
Generally, participants of both treatment groups seem to agree on which attribute levels they prefer. So I was thinking about comparing attribute importances. However, as the attribute importances are relative and add up to 100% within each treatment group, comparing attribute importances across treatment groups does not allow me to draw inferences such as attribute A is more important to treatment group 1 than to treatment group 2 (in absolute terms). To draw such conclusions I would have to compare the absolute range of partworth-utilities for an attribute across the treatment groups, right?
What is the best way to do this or generally to compare treatment groups?
I was thinking about plotting the part-worth utilities of both treatment groups in one graph so as one can see, which treatment group has a "steeper" curve/ stronger preference of one attribute level over the other, compared to the other treatment group.
Which parth-worth utilities do I use here? The ones of the separate HB analysis?
Thank you very much for your help! :)