Suzy:
Simulations are a great way to answer questions like these, and to built your intuition about how
these things work. Make a simulation where the probability of missingness is constant or random
and run that regression. Another thing you could do is make the missingness probability depent
upon age and age squared, and omit the squared term from the logistic model you estimate to see
the effect of misspecifying the model. You could use the -simulate- command to to do this many
times. Also you could investigate the effects of NMAR by making the probability of missingness
dependent on BMI.
Have fun,
Maarten
--- Suzy <[email protected]> wrote:
> What I also did is dichotomize bmi missingness -
> (generated newvar bmicat = 1 missing ; 0 otherwise). I then ran a
> logistic regressions with bmicat as the binary response variable
> univariately (age alone, sex alone, race alone, etc...) and then with
> the full model. In each case, the odds of BMI missingness was
> significantly associated with age, but not with any other variables. Age
> was even associated with bmicat in the full model after accounting for
> the other variables). I heard that this is an approach that can be used
> to assess MCAR vs. MAR. Do you agree?
-----------------------------------------
between 1/2/2006 and 31/3/2006 I will be
visiting the UCLA, during this time the
best way to reach me is by email
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting adress:
Buitenveldertselaan 3 (Metropolitan), room Z214
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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