28 Oct
2012
28 Oct
'12
10:50 a.m.
Nicole, I think this is the principle you're after: your imputation model
should include all the variables that could predict which items are
missing. This should then include all the variables you'd use in your
analysis models.
It's generally a good idea to impute once (or as many times as you like
until you conclude you get it right), and then to take that one set of
imputed data (i.e., the one collection 5 or so imputed data sets) and run
as many analysis models as you like from that starting point.
Gary
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On Sat, Oct 27, 2012 at 3:50 AM, N. Janz <nj248(a)cam.ac.uk> wrote:
Dear all,
I have two questions and would be very grateful for your help:
1) Is there a problem with running imputations on different subsets of
your full data set when I use the same variables in my models from
different imputations?
2) Do I have to include lags in the imputation specification that I expect
I 'might' use in my models (although I'm not sure yet)? For example, all
independent variables 'might' be lagged one year to allow for their effect
to 'spread' to the outcome variable. If I don't include them and decide
to
use lags after a first run of imputations, do I have to go back to Amelia,
include the lags, and run it again?
Best,
Nicole
Nicole Janz, PhD Cand.
Lecturer at Social Sciences Research Methods Centre 2012/13
University of Cambridge
Department of Politics and International Studies
www.nicolejanz.de | nj248(a)cam.ac.uk | Mobile: +44 (0) 7905 70 1 69 4
Skype: nicole.janz
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