st: GLLAMM and rescaling sampling weights

[Susanna Makela <susanna.m.makela@gmail.com>]

Dear Statalisters,

Apologies in advance if this is a double posting; I sent this email a
few days ago but haven't seen the message show up in the archives or
the statalist digest, so I'm retrying.

Some background: I am using GLLAMM to run a multilevel logistic
regression on a round of DHS (Demographic and Health Survey) data. I
have a three-level model with children as level 1, PSUs as level 2,
and state as level 3. Because my unit of observation is children and
because I am pooling data across states, I am using the national-level
women's weights as my level-1 sampling weights (since information on
children comes from interviewing their mothers). I am assigning
sampling weights of 1 for the PSUs and states since GLLAMM requires
weights to be specified for all levels of the model.

The GLLAMM manual notes that the pweight option, which holds the
sampling weights, "should be used with caution if the sampling weights
apply to units at a lower level than the highest level in the
multilevel model. The weights are not rescaled; rescaling is the
responsibility of the user."

My questions are:

- What is the appropriate way to scale sampling weights when they
apply to the lowest level in the model?

- Do I have to adjust for the fact that, in my particular case, I am
assigning the same weight to all children of the same mother?

I've read "Multilevel modeling of complex survey data" (Rabe-Hesketh
and Skrondal, J. R. Ststist. Soc. A 2006), but didn't quite understand
all of it. The published papers I've found that use both DHS data and
GLLAMM don't discuss how - if at all - they rescale the sampling
weights.

Thanks!

-Susanna
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