I ran a GEE model, with a dependent variable of "percent of total students with an unexcused absence," using a binomial family. My dependent variable is basically a proportion, with range from 0 to 1 to obey the rules of the binomial family.
I understand that the difference between the non-event/event 0-1 is "zero percent of students with an unexcused absence" and "100% of students with an unexcused absence."
- How can I interpret the Odds Ratio for this in a way that makes policy-level, real-world sense?
An interpretation of 1-unit change in independent variable X would be e.g. "a school with a 1-unit increase in X has 0.9 times the odds of having 100% of students unexcused from class at least once, compared to a school without the increase in X." Seems clunky!
You may ask why I used a binomial family rather than a Gaussian family, but according to "Comparison of Logistic Regression and Linear Regression in Modeling Percentage Data" by Zhao, Chen and Schaffner (2001), logistic regression can and should be used for any models with a dependent variable that's modeled as a percentage for various reasons.
If there's no other accurate way to interpret this coefficient through logistic regression and ORs, does anyone have any suggestions about how to model the data to be better interpretable?