8 May
2002
8 May
'02
11:31 a.m.
On Wed, 8 May 2002, Matthew Vile wrote:
Dr. King,
I hope this email reaches you at a convenient time. I am student
working on my dissertation in public opinion and I have been considering
using your program Amelia to sovle some missing data problems.
My question is - generally speaking, what is the maximum "missingness"
that Amelia can reasonably handle. I have read your APSR article (King
et al 2001), and I see that your Monte Carlo sims relied on a dataset in
which @ 5% of the data were missing. In your opinion, could Amelia
impute to a variable in which 50% were missing, 66%? Under what
conditions would you feel comfortable doing this?
Specific example - the variable measuring fear of assault was only asked
to approxiamtely 700 individuals out of 1800. presuming I have
indicators of this variable (I do have some) would you feel comfortable
impute to the missing 1100 cases?
I appreciate your consideration and any advice you might have.
The answer depends on both the pattern and the level of missingness, but
Amelia should work without a problem on the application you describe. The
more data are missing, however, the more model-dependent your results will
be. Of course, that would be true whether you use Amelia or any other
method.
Best of luck,
Gary King
: Gary King, King(a)Harvard.Edu http://GKing.Harvard.Edu :
: Center for Basic Research Direct (617) 495-2027 :
: in the Social Sciences Assistant (617) 495-9271 :
: 34 Kirkland Street, Rm. 2 HU-MIT DC (617) 495-4734 :
: Harvard U, Cambridge, MA 02138 eFax (928) 832-7022 :
-
amelia mailing list served by Harvard-MIT Data Center
List Address: amelia(a)latte.harvard.edu
Subscribe/Unsubscribe: http://lists.hmdc.harvard.edu/?info=amelia