Hi all, I am running a set of logistic regressions, where we want to use some weights, and I am not sure whether what I am doing is reasonable or not.
The dependent variable is turnout in an election - i.e. survey respondents were asked whether or not they voted. The percentage of those who say they voted is much higher than the actual turnout, probably due both to non-response bias and social desirability issues. So now the suggestion is to weigh the cases, to weigh down the respondents who say they voted and weigh more heavily those who did say they did not vote. So the questions that arise from this are: 1) Is it reasonable to use the distribution of the dependent variable to calculate the weights used in a logistic regression? It feels wrong, but I cannot find, so far, any sources on this. 2) How to implement this in R? I tried the weights option in glm(), but I think that is meant for when you have one row in your data for multiple observations, not for this kind of weight. Although I have the McCullagh and Nelder book explaining in detail how glm() operates, I cannot find a similar book for svyglm(). Is svyglm() better for this type of weighting? 3) Where would I find a good source describing the estimation procedure, including weighting, applied in svyglm()? Thanks in advance for any help! Jos -- Johan A. Elkink Lecturer in Social Science Research Methods School of Politics and International Relations & CHS Graduate School University College Dublin Ph. +353 1 716 8150 | Newman Building, Rm F304 http://jaeweb.cantr.net ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
