I run an HB analysis giving me the individual-level utilities. To account for possible differences between males (n=182) and females (n=89), I've calculated the mean part-worth utilities and standard deviations of each of the groups (m/f).

I want to ask you whether it is the correct way to "simply" do a t-test by averaging the values for each attribute level (averaging mean part-worths males and females and standard deviation), calculating the other important values (i.e. standard error) and checking whether the final results are +/- 1.96 on the 95% confidence interval.

I would be great to hear from you soon :-)

Best regards!

I've done so, but I think I rather have to take the difference between the average utilities of the two groups, and then I divide the difference of the mean part-worts by the standard error (average standard deviation between the groups/ square root of sample size (n-1)), instead of the average between the mean part-worth utilities, don't I?

Also, for my further analysis can I / should I proceed with this kind of segmentation (f/m) for HB, i.e. testing RLH, Percent Certainty, hold out choice sample and co. for each of the groups, or is it acceptable / correct to test the model accuracy and quality without focusing on each group?