My data contain a bounded continuous variable (score between 0 and 10) representing the efficacy of a given method to control an invasive species. As there are more high scores than low ones, the variable is skewed to the left. My purpose is to identify which independent variables explain this dependent variable. In other words, I would like to know which variables affect the efficacy of the method.
According to some answers I found on CV (e.g. here), a model with a beta-distribution family could be used to model the relationship between my variables. However, considering that my dependent variable is fairly skewed and that I have a small sample size ($n$ = 98), I was wondering:
- If a model with a beta-distributed response would be the most appropriate option here? (Provided that I transform my DV so as to lie between 0 and 1, but with no 0 and no 1, right?)
- If there was a sort of rule of thumb regarding the minimum number of observations per predictor possibly included in this type of model?