Re: st: Multiple Imputation in Longitudinal Multilevel Model

[Stas Kolenikov <skolenik@gmail.com>]

1. Are read1-read3 and math1-math3 three measurements taken at the
same time for a given individual, or measurements taken over three
periods? If the former, then your model is "flat", as it does not
recognize and utilize the longitudinal/multilevel nature of the data.

2. Once you've done -ice-, don't touch anything (let alone anything as
drastic as -drop if _mj==0-), and use -mi: estimate- for everything. I
don't really know how well either -mi- or -ice- go with -reshape-, but
I suspect that if not done properly, it will screw up the delicate
mechanics of -mi-.

3. I agree with Jay that 4 imputations are woefully insufficient. I
have heard the arguments that you don't see much Monte Carlo
variability beyond 5 imputations, but I can put two arguments in favor
of a much greater number, like M=50: first, you don't explore the
multivariate space of missing data enough (M=5 may be OK for a
univariate mean, but I can't see how it can work for a 30-dimensional
space), and second, I want my minimum degrees of freedom to be greater
than the nominal sample size, so that the limitation on the accuracy
really comes from the data rather than the computer.

4. If you are bringing additional variables to the -xtmixed- model,
you would probably have been better off using these variables in
imputation. You had a reason to believe that they affected the
response, and for that same reason they should be in the imputation
model.
-- 
-- Stas Kolenikov, PhD, PStat (SSC)
-- Senior Survey Statistician, Abt SRBI
-- Opinions stated in this email are mine only, and do not reflect the
position of my employer


On Tue, Mar 5, 2013 at 10:09 PM, JVerkuilen (Gmail)
<jvverkuilen@gmail.com> wrote:
> Anthony,
>
> I would definitely recommend looking at the official release MI commands.
>
> I also don't think that 4 imputations is anywhere near enough. Unless
> there's something extraordinarily odd about your dataset or computer,
> it should be able to cope with many more imputations. That said, with
> longitudinal data developing a reasonable imputation model is not an
> easy task. You often need to handle time quite carefully to ensure
> that trends don't become too strange, for instance, and be very sure
> you've done a thorough outlier screening.
>
> Jay
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