Maarten,
Thanks a lot, this paper shows that a hierarchical approach is the one I should take in order to correctly deal with missing values in multiple surveys.
Victor.
----- Original Message -----
From: Maarten buis <[email protected]>
Date: Saturday, July 11, 2009 4:16 am
Subject: Re: st: multiple_imputation
To: [email protected]
> --- On Sat, 11/7/09, Victor Mauricio Herrera wrote:
> > I am working with a pool of surveys and I want to impute
> > missing values in the pooled dataset while keeping the
> > design variables and re-calculated weights. Here is my
> > question: Is it correct to impute missing values - and used
> > them during the analysis - for variables that were not
> > originally measured in some of the surveys? For example,
> > waist circumference was not measured in all the surveys,
> > however, since that variable must be included in the
> > imputation model all individuals in the pooled dataset end
> > up with a value for that variable.
>
> In general I like this approach; this way you can also use
> the information available in surveys that only asked some
> of the questions. In practice getting a good imputation
> model can take quite a bit of puzzling. In particular
> trying to incorporate characteristics of the surveys can
> become quite tricky. If you need a reference of someone
> who has done this before see (Gelman, King, and Liu 1998),
> though their exact imputation model has not been implemented
> in Stata.
>
> Hope this helps,
> Maarten
>
> Andrew Gelman; Gary King; Chuanhai Liu (1998) Not Asked and
> Not Answered: Multiple Imputation for Multiple Surveys.
> Journal of the American Statistical Association,
> 93(443):846-857.
> http://www.stat.columbia.edu/~gelman/research/published/gelmankingliu.pdf
>
> -----------------------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
> http://home.fsw.vu.nl/m.buis/
> -----------------------------------------
>
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