--- mählmann wrote:
> I have a dataset with one variable (x) partially missing. I want to
> test if the missingness mechanisms is nonignorable (data is MNAR), ie if
> P(r=1|x,z) does depend on x, where r is the binary missingness
> indicator and z are completely observed covariates.
Thomas:
Short answer is: "you can't test the MAR/NMAR assumption".
Slightly longer answer is:
You want to test whether or not x is missing depends on x itself.
In order to test that you would need to compare the values of x
when x is observed and the values of x when x is missing. So you
would need to know the values of x when x is missing. If you would
know that, x wouldn't be missing... So MAR is an untestable
assumption.
A common (and in my humble opinion reasonable) way around this
is to assume that missingness on x may depend on lots of other
(fully or partially observed) variables, and assume that after controlling
for these other variables there is nothing left for x itself, i.e. the MAR
becomes more reasonable when you control for more covariates.
HTH,
Maarten
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting adress:
Buitenveldertselaan 3 (Metropolitan), room Z434
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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