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st: Second Repost "Simple Cases of Multi-Level Models"
My question concerns the XT series of models in STATA. My question is
whether the XT series allows one to treat second-level variation as
something to study, rather than simply as a nuisance to be controlled.
Given my interest in the former, I read with interest the
presentation/paper titled "Simple Cases of Multi-Level Models" by Richard
Goldstein at a STATA Users Group Conference, which seemed to suggest that
the XT series may meet my needs. I presume attending would have clarified
my question, but given that I was not there I have a question.
My question probably flows from the many different meanings that are
floating around for the terms that are in use in different disciplines.
I am specifically interested in the statement on a slide from the talk,
that reads "I know that the following is true for at least some of the XT
models and I believe it is true for all of them: they can be used to
analyze any two-level random intercept model."
My aim is to estimate a model of the following form:
Pr(Y_ij=1) = b1_j White1_ij + b2_j Black1_ij + b3 X1_ij + b4 X2_ij + e_ij
b1_j = g01 + g11 Z1_j + g21 Z2_j + d1
b2_j = g02 + g12 Z1_j + g22 Z2_j + d2
b3_j = g03
b4_j = g04
In this model the Zs are measured at the higher level; there are a few
dozen higher-level entities and lots and lots of lower-level units. The
aim is to estimate the "effect" of Zs on the black and white intercepts.
That's the set-up. The questions: can this be estimated using the "XT"
models in STATA? And, if so, how?
Thanks for any references to GLLAMM, but I am familiar with GLLAMM. Any
insights on this in terms of the XT series of models are greatly
Thanks a bunch.
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