I have proportion data (percentage viewership of TV programs) that i'd like to model as a function of various demographics (age, sex etc.) and time (year). After surveying options for appropriate multiple regression models, I'm debating between the following two strategies:
1) fit a beta regression model after dividing the percentage data by 100 and adjusting the range slightly so that values of zero and one do not occur.
2) fit an OLS model after logit transforming the percentage data (again, divided by 100 and adjusted slightly) so that the dependent variable is mapped to the Real line.
One key consideration is that i'd like to make the results as intuitive as possible to a non-statistical audience. So, interpretations such as "for a one unit change in X we get a percent change in Y", or something like that, would be most welcome.
Can anyone outline pros and cons of these two approaches in this regard?
It seems to me that using beta regression with a logit link, and then calculating odds ratios may lead to nice "percent change" explanations. The coefs from the OLS model would also be on the ln(odds) scale, so I assume I could also do the same for that model. My data are too large to share, but I ran both models in R and there are only minor differences in the coefs.