I got my data from a questionnaire: group 1 had 30 individuals and group 2 also 30 individuals. They answered the same 6 questions where the opinions of others were exposed and after they could provide a final decision on those questions. Thus I could calculate weights on opinions (my DV) that range from 0 to 1 (continuous data), namely
WOA = (final estimate $-$ initial estimate )/(advice $-$ initial estimate).
DV = 0 means that participants stick to their initial estimates; DV = 0.5 means that they compromise; DV = 1 means that they adopt fully the other's opinion.
At the end, I got 6 different WOAs (per question).
The IV group is a dummy variable where 0 is group 1 and 1 is group 2.
The data look like this:
I have found from previous posts here that a generalised linear model with binomial family, logit link and robust standard errors would be the most appropriate for an analysis.
But since my DV is a continuous variable is then a fractional logit model the best? If yes, but I have multi-level data: individuals (30 - 30), group ( 2) and type of questions/treatments (6). Would it be then right to do a fractional logit if I do not control for the level (type) and plus my distribution is not normal?
According to O'Hara, R. B. & Kotze, D. J. 2010. Do not log‐transform count data it is better to use a GLM with negative binomial family.
I am quite confused. I appreciate any help you can provide!
Plus, a bit out of the regression topic, since the means do not tell me much about the data, would it be better to present in the analysis the medians of those WOAs as a descriptive analysis?