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

Thank you Maarten. 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?

tab bmicat

bmimi | Freq. Percent Cum. ------------+----------------------------------- 0 | 305 91.87 91.87 1 | 27 8.13 100.00 ------------+----------------------------------- Total | 332 100.00

logistic bmicat age sex fhdm dmcat race

Logistic regression Number of obs = 332 LR chi2(5) = 37.96 Prob > chi2 = 0.0000 Log likelihood = -74.639705 Pseudo R2 = 0.2028

------------------------------------------------------------------------------ bmicat | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | 1.121633 .0268219 4.80 0.000 1.070276 1.175454 sex | .9201524 .4542155 -0.17 0.866 .3496878 2.421247 fhdm | 1.060376 .5558315 0.11 0.911 .3795549 2.962413 dmcat | .7724482 .4741646 -0.42 0.674 .2319329 2.572625 race | 1.340202 .791231 0.50 0.620 .4213434 4.262891 ------------------------------------------------------------------------------

Maarten buis wrote:

>Suzy: >You wanted to create missingness according the to a MAR process, in your case the probability of >missingness in the variable bmi should depend on the variable age. So we created the probability >of missingness for each observation. The youngest person in your dataset has a probablity of >missingness of invlogit(-8 + .1*28) = .0054863 (type -di invlogit(-8 + .1*28)-) and the oldest >person has a missingness of invlogit(-8 + .1*82) = .549834. If the probability of missingness was >constant (or random and unrelated to any of the other variables) than the missingness mechanism >would be missing completely at random MCAR. > >HTH, >Maarten > >--- Suzy <scott_788@wowway.com> wrote: > > >>I'm not sure what the implications are of the std dev and the >>max values of p (.549). >> >> > >----------------------------------------- >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/ >----------------------------------------- > > > > > >___________________________________________________________ >Yahoo! Messenger - NEW crystal clear PC to PC calling worldwide with voicemail http://uk.messenger.yahoo.com >* >* For searches and help try: >* http://www.stata.com/support/faqs/res/findit.html >* http://www.stata.com/support/statalist/faq >* http://www.ats.ucla.edu/stat/stata/ > > > > >

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