[R] (svy)glm and weights question

[Jos Elkink]

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