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RE: st: Missing outcome variables - how to deal with these?


From   Ana Gabriela Guerrero Serdan <ag_guerreroserdan@yahoo.com>
To   statalist@hsphsun2.harvard.edu
Subject   RE: st: Missing outcome variables - how to deal with these?
Date   Sat, 23 May 2009 03:10:58 -0700 (PDT)

Dont know if this applies to your type of data but if you have survey data you can first see how much selection for those individuals where you have missing information. How different they are from the rest, compare the rest of the characteristics where you do have some information. So do a test to check this.  

One thing you would be able do if you have missing values for some of the explanatory variables  (which is not your case) is to create a dummy =1 for those variables that you have missing values, in this way you dont loose the observations when you do your analysis, for example, if you do a regression and your outcome variable is education and you want to include an explanatory variable of education of the mother/father but you have missing values here, then you include the dummy that I was mentining before. 


hope it helps, 

regards, 
Gaby 

--- On Sat, 5/23/09, Maarten buis <maartenbuis@yahoo.co.uk> wrote:

> From: Maarten buis <maartenbuis@yahoo.co.uk>
> Subject: RE: st: Missing outcome variables - how to deal with these?
> To: statalist@hsphsun2.harvard.edu
> Date: Saturday, May 23, 2009, 3:02 AM
> 
> --- On Fri, 22/5/09, Tomas M wrote:
> > For my data, I am quite certain that the data is not
> > missing at random (NMAR).  I have reason to
> believe
> > that my missing outcome data is related to the outcome
> data
> > itself.  I do have a full set of explanatory
> variables
> > for all of my observations, however.
> > 
> > Does this mean that I cannot use the typical
> > remedies?  What other options are there for
> analyzing
> > missing data that is non-ignorable?
> 
> I have always stayed away from those NMAR models. The
> problem
> is that they just can't produce empirical estimates: They 
> critically depend on something that can't be seen. I
> realise
> that there are questions out there that are so important
> that
> we must just give the best "guesstimate" we can, even
> though
> under normal circumstance that best guess would not be 
> considered good enough. Till now I have been able to avoid
> those questions, so I don't know the answer to your
> question.
> 
> -- Maarten
> 
> -----------------------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
> 
> http://home.fsw.vu.nl/m.buis/
> -----------------------------------------
> 
> 
>       
> 
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