Friday, February 24, 2006

Re: st: Re: simple way to create missing data that is "missing at random" from a small datset

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 <> 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 -----------------------------------------

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