# How to analyze right skewed data with a continuous DV?

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:

The data area not normally distributed: most of the estimations are close to 0. For example, the WOAs for 2 different questions:

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.