increasing the number of imputations will help with simulation error if
you have lots of missingness.
but the big problem in this situation is model-dependence. you don't want
your answers to depend heavily on your choices of an imputation model.
but the more missingness you have, the more model dependent your
inferences will be. this is true whether you use Amelia II or any other
method. there isn't much you can do about this other than either (a) go
out and collect some of the missing observations, and/or (b) remove
imputations that require inferences outside of or far from the convex hull
(see the first 2 papers at
http://gking.harvard.edu/projects/cause.shtml)
Gary
On Thu, 24 Apr 2008, Gustavo de las Casas wrote:
Is there a cut-off for rate of missingness, past which
we should employ other
methods (i.e. Not Amelia 2)? Or does it depend on the diagnostic results?
More specifically, if my imputations:
a) don't give me error 34 (which says there is not enough data to do
imputations
properly) and;
b) my diagnostics seem kosher (distributions of imputed/actual observations
overlap nicely, there is convergence, etc.),
can I relax about the rate of missingness in the original data?
Simply: I got a time-series, cross-sectional dataset. 10 years, 50 countries.
6 independent vars. Of the 6, 3 have 65% missingness. Yet, these 3
independent vars with 65% missingness have significant relationships with the
rest of the vars, and Amelia 2 was able to give me a decent-looking
imputation. [I can offer the misschk results from Stata if necessary to
answer this question.]
Is there a cut-off in the fraction of missingness past which I must worry? Or
Amelia would have already told me so?
King also mentions that upping the imputations (to, say, 10) can help deal
with higher rates of missingness. Something I should do just to make sure?
You can also direct me to somewhere in the literature where you think this is
specifically addressed. Thanks much.
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