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RE: st: stepwise
..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
Williams
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
OFFICE: (574)631-6668, (574)631-6463
FAX: (574)288-4373
HOME: (574)289-5227
EMAIL: [email protected]
WWW (personal): http://www.nd.edu/~rwilliam
WWW (department): http://www.nd.edu/~soc
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