I am doing an analysis on the impact of services. I could have done this by simply looking at those who got the service and those that did not (there are a series of services). But it did not make sense to me to test who got a service versus who did not. Instead it made more sense to test who got the service who was eligible for the service (a professional judgement of counselors) against those eligible for a service who did not get it (as a separate issue there are many statistical controls built into the model) . For example I tested the impact of getting a transportation service by looking at who was deemed to need it by counselors (only about forty percent of those who were eligible for that service got it). This is done in linear regression (income is the DV).
To me that approach makes sense, I wondered what others thought of it. But I have a second question. Many services are offered and they impact (I would guess customers at the same time). But I can't figure out how to include the different services into one model. The reason is that I am testing only those who are eligible for a service. So the population will vary with every service.
Any suggestions how I can address this issue. Or should I just ignore being eligible for a service and just run if getting a service matters or not, ignoring eligibility entirely?
To me that approach makes sense, I wondered what others thought of it. But I have a second question. Many services are offered and they impact (I would guess customers at the same time). But I can't figure out how to include the different services into one model. The reason is that I am testing only those who are eligible for a service. So the population will vary with every service.
Any suggestions how I can address this issue. Or should I just ignore being eligible for a service and just run if getting a service matters or not, ignoring eligibility entirely?