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Re: st: Multilevel longitudinal analysis with censored data
From
Gordon Hughes <[email protected]>
To
[email protected]
Subject
Re: st: Multilevel longitudinal analysis with censored data
Date
Wed, 23 Feb 2011 10:03:40 +0000
Just to be clear about the problem. You refer to longitudinal
achievement as measured by letters in the alphabet but I assume that
these have some kind of ordinal significance, otherwise you are just
dealing with a set of categories. So suppose that you have measured
the reading age of each child on a standard scale from 5 to 15 in
which category 15 means a reading age of >= 15.
In that case, I don't think that you will find a pre-packaged routine
to carry out multi-level analysis. You should look at the
user-written package -gllamm- which is documented in a manual and
book by Rabe-Hesketh et al (the manual is available via the UC
Berkeley website and there is a paper in the Journal of Econometrics
2005). Also, note the double "l" and double "m". It can be
installed from SSC : use -ssc describe gllamm- or -ssc install
gllamm-. This does not have a multi-level version of tobit but it
does have other specifications which might be adapted to your purpose.
However, before committing yourself to elaborate and quite time
consuming methods of analysis, you should think about the
specification of your model.
A. You have only 4 observations over time. This is very small for a
development path with 26 different categories.
B. Actually, you don't have 4 observations on every child since you
say that you have used imputation to fill in gaps, which would worry
me even more.
C. Many multi-level models can be re-written mathematically as
conventional panel data models, in which case you could use
-xttobit-. It really depends upon what assumptions you want to make
about the structure of the random coefficients in the model.
D. Anyway, is the top-level censoring (assuming that is what it is)
really significant, given the other limitations of your data and
model? If there is a lot of censoring, then why not think about use
of -xtmelogit- to examine who reach the highest level of achievement?
Gordon Hughes
[email protected]
Date: Tue, 22 Feb 2011 13:40:24 -0500
From: Bernadette Puckett <[email protected]>
Subject: st: Multilevel longitudinal analysis with censored data
Dear Stata list,
I am currently conducting an analysis on the relation between school
quality and academic growth across 4 time points. Time point is
clustered in children and children are clustered in school. I also
include fixed effects for district (i.district). I have imputed the
data to account for missingness across time periods within children
(there is no missing data are the school level).
The issue is that the longitudinal achievement has an upper limit
(e.g. cannot exceed 26 letters in the alphabet).
This is my current model without accounting for the ceiling effects:
mi estimate: xtmixed achievement time quality##time i.district ||
school: || child_id: time, variance cov(un) mle
My question is how to conduct a multilevel longitudinal analysis with
censoring (similar to a tobit, using imputed data),
Thank you,
Bernadette
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