Re: st: RE: RE: RE: Imputing values for categorical data

[Richard Goldstein <richgold@ix.netcom.com>]

There is a problem with the indicator variable method for missing values.  See 
Jones, MP, (1996) "Indicator and Stratification Methods for Missing 
Explanatory variables in Multiple Linear Regression," JASA, 91: 222-230.

Rich Goldstein

Dupont, William wrote:


> Bill
>
> What you propose sounds reasonable to me.  However, I recently submitted
> a paper that did this and was trashed by a referee, in part because of
> how I was handling missing values.  Using an indicator variable for
> missing values has the advantage as it gives you some idea as to whether
> you are, in fact, dealing with nonignorable missing data.  However, this
> approach does not appear to be in fashion at this time.
>
> My own approach to data analysis is to attempt to use methods that
> 1.  I think are reasonable,
> 2.  are widely accepted within the biostatistical community, and
> 3.  avoid ignoring or attacking sacred cows that are dear to likely
> referees.
>
> My own sense is that, at least in medical statistics, multiple
> imputation is becoming a very popular way of dealing with missing data.
> I also feel it is a sensible approach, particularly if it is only used
> for confounding variables or if your study design gives you reason to
> believe that the missing data is missing at random.
>
> I would be interested to know what other Statalisters think about using
> indicator variables to model missing values.
>
> Bill
>
>
>
> -----Original Message-----
> From: bill magee [mailto:magee@chass.utoronto.ca] 
> Sent: Friday, April 16, 2004 5:11 AM
> To: Dupont, William
> Subject: RE: RE: RE: Imputing values for categorical data 
>
> Hi Bill --
> Rather than imputing a missing categorical control or confounding 
> variable, such as gender, wouldn't it usually be better to just include 
> a category for missing (e.g. a dummy for female, a dummy for missing, 
> with male as the excluded contrast group)?
>
> bill magee
>
>
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