Dear all,
I'm investigating on how to estimate a specific kind of cross-
classified multilevel model. I think it is often referred to as a
multiple-membership model.
To problem is this: I want to study changing attitudes of people, for
which I use (amongst other things) contextual data of the
neighborhood they live in. I have two surveys (panel-design) and I
know the neighborhood people lived in during both surveys.
Until this point, it can be estimated using simple multilevel
modeling. The problem is that many people moved to other
neighborhoods (I still have all their information). `Basic' cross-
classified models (with contexts of both neighborhoods) do not
suffice, for two reasons:
- not all respondents moved, and I want to model them all
simultaneously
- not all respondents moved at the same time, so weight-factors need
to be applied to contexts
Does any of you have ideas on how to estimate such models? Preferably
using the NLME / LMER packages. In the literature these kinds of
models are referred to as multiple membership models.
Thanks in advance,
Rense Nieuwenhuis
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