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RE: st: stepwise

From   "Feiveson, Alan H. (JSC-SK311)" <[email protected]>
To   <[email protected]>
Subject   RE: st: stepwise
Date   Fri, 1 Sep 2006 10:08:33 -0500

..however if there is a large amount of missingness, so that listwise
deletion would result in very few observations, one could try multiple
imputation and work with a "complete" set. This might do a better job of
identifying a "good" model Once you have this model, then you could use
whatever cases are really present.

Al Feiveson

-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Richard
Sent: Friday, September 01, 2006 10:50 AM
To: [email protected]
Subject: Re: st: stepwise

At 09:28 AM 9/1/2006, [email protected] wrote:
>Hi Stata list,
>When it come to stepwise regression, both SPSS and Stata do something I

>don't know why it does. Given the made-up dataset below, where y has 8 
>observations and x1 has 8 and x2 has 7.
>    y      x1    x2
>     4.00            1.00             .00
>     5.00            1.00             .00
>     6.00            1.00             .00
>     7.00            1.00            1.00
>     8.00             .00             .00
>     9.00             .00             .00
>    10.00             .00        .
>    11.00             .00             .00
>If I run a stepwise regression of y on x1 and x2, using the Forward 
>procedure, then the final 'selected' model, which is equivalent to the 
>y on x1 regression, only uses 7 observations. Is it based on any 
>statistical principle that models should be selected this way? If not, 
>why does Stata not at least provide an option where you can use all 
>available observations in the selection process?

I believe both SPSS and Stata start by doing listwise deletion of MD.
When choosing a model, the comparisons would get distorted if different
cases were being analyzed at different steps, i.e. you shouldn't compare
a model with 8 cases and x1 to a model with 7 cases and X1 and X2.

Richard Williams, Notre Dame Dept of Sociology
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