Maria,

I don't know of references off-hand, but it seems like a bad idea on
the face of it.

Do I understand correctly that the analysts are wanting to give a
single value to all missing data, and then analyze?  That's making two
very strong and implausible assumptions: that all the missing values
represent the same level of income, *and* that you know with precision
what that value is. (Multiple) imputation represents a balance between
what you can be fairly sure of -- a general idea of income based on
the other variables -- and uncertainty around that general idea.

Hope that helps,
Pat

On Sat, Jul 4, 2009 at 9:19 AM, Maria da
Conceicao-Saraiva<[email protected]> wrote:
>
>
>
> Sorry about this question,
>
> I have been discussing with some people I am working about the need of
> imputation with some of our data. What some of analysist are doing is just
> to creating a category of missing values inside some variables, they argue
> this is enough. It has been hard to argue with them that this is not the
> best way to do. Specially in our variable income, we have about 30% of
> missings.
> Does anybody know about ?refereces discussing this approach of just creating
> a category for missing values inside a variable?
>
> Maria
>
>
>
>
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
> Maria da Conceicao P. Saraiva DDS, MSc, Ph.D
> Departamento de Clinica Infantil e Odontologia Social e Preventiva
> Faculdade de Odontologia de Ribeirao Preto-Universidade de Sao Paulo
>
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-- 
Patrick S. Malone, Ph.D., Associate Professor
Department of Psychology
University of South Carolina
http://people.cas.sc.edu/malonep/
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