> I am a PhD student in sociology, studying in Essen, Germany. I am doing an
> empirical research with data on three (hierarchical) levels: Individuals
> within schools within regions. The data are very sparse with a lot of
> schools containing only one individual. In addition the dependent variable
> is dichotomous, so that a logistic regression would be appropriate. There
> are independent variables on all three levels. After studying the related
> literature it seems to me that a (non-linear) random-effects model might be
> most appropriate, there being not enough cases to do a more detailed model
> (that distinguishes within- and between-cluster regression) like a
> random-coefficients model. I also found out that this might be done with
the
> NLME-package in R. But having no one in my environment who is working on
> similar models or with the R software in general and having no experience
at
> all in programming, calculating this model with R is more than I can handle
> on my own. Has anyone got an idea how I could speed up the process? Are
> there any workshops? Other people working on related topics for cooperation
> or help with the model specification in R language?
1. It's your PhD supervisor's job to help you with the data analysis.
Get him to help you. If he won't help, you need a new supervisor.
2. Get the book and copy the example most similar to yours. You and your
supervisor can learn this together.
`Mixed-Effects Models in S and S-PLUS'
J. C. Pinheiro and D. M. Bates
Springer. ISBN 0-387-98957-9, 2000.
3. Another possibility is to use software from J. Lindsey:
http://alpha.luc.ac.be/~lucp0753/rcode.html
He is at univ of Liege--maybe he runs a course on this sort of modelling
and you can zip over there to sit in on it...
BTW the bit about your sparse data doesn't sound very promising. Your
supervisor can tell you what you need to do about it.
Bill Simpson
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