I have to explain my data before I can ask my question. I have survey data on volunteering. The data were collected using an RDD methodology. The data suffer from two problems -- non-response bias (people opting out of the survey) and response bias (people giving the socially accetpable answer). I can't tell the degree to which either impacts my estimations, but I know they do. In addition to answering questions about volunteering, the respondents were also asked if they voted in the last presidential election (the data were collected in the spring of 2001, not long after the election of 2000). Seventy percent (70%) of the respondents said they voted, which is much higher than the 51% who actually voted. I don't know if my higher voting rate is a non-response bias or a response bias, just that it's too high. I also know that 44% said they volunteered. With me so far?
What I want to do is adjust the volunteering rate to correct for the known bias. There is support in the literature for adusting a sample to known population parameters, something that is done frequently when a sample is adjusted to fit paramters such as gender, age, race, etc., but I can find nothing that talks about using an embedded question proportion to adjust another proportion. In other words, I want to adjust the sample so that 51% are voters, thereby gaining a more accurate estimation of the percentage who are volunteers. Still with me? I can do a simple ratio adjustment (51 is to 70 as X is to 44), but that doesn't take into account the fact that some people are more likely to be volunteers than are others. I've been struggling with logistic regression as an approach to this, but without success. Does anyone have any suggestions on how I can approach this? Thanks, Chris Chris Toppe, Ph.D. Director, Philanthropic Studies Independent Sector [EMAIL PROTECTED] . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
