What justifies the usage of a variable for post-stratification?
I am working with a constituent survey of a non-profit's constituent with 2500 responses out of a much larger sample and even larger population. I have many variables about the target population, which are all active constituents. In literature I've read, it's common to use demographic variables (age, gender, and race, for example), but in my experience with this data, demographics have relatively high data quality errors and weak correlation to non-response error, while behavioral data (for example, donation history) are recorded reliably and correlate better to non-response.
I assume demographics are common because many surveys try to get a nationally representative sample, and the government publishes demographic information for this population.
Because I have them, is there anything wrong with using the behavioral variables instead of, or in addition to, the demographics? Is there a practical empirical method to choose variables?
If the suggestion is to use behavioral variables in addition to demographics, how would I detect or prevent overfitting when raking weights with many variables?