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RE: st: RE: Testing nested models using logistic regression with robust standard errors


From   Richard Williams <Richard.A.Williams.5@ND.edu>
To   statalist@hsphsun2.harvard.edu, <statalist@hsphsun2.harvard.edu>
Subject   RE: st: RE: Testing nested models using logistic regression with robust standard errors
Date   Mon, 28 Apr 2008 18:20:09 -0500

At 04:47 PM 4/28/2008, Lachenbruch, Peter wrote:
There may be a greater horror in store for us when we try to develop
models - if there are missing values, the number of observations in each
of the runs likely will differ.  The variables you select will depend on
the order in which you drop them...  there are no good solutions for
this.  I've tried one possibility which is to require e(sample)=1 from
the full model and then continue - this is equivalent (I think to a
backward stepping model which is yukky.  Another possibility is to use
multiple imputation and then drop the least significant variables. You
can't use backward stepping, but it's a simple process with ice and mim.

Anyway, beware the missing value.
To keep the N the same, and if vars are being entered in blocks, you can also just use nestreg, e.g.

. use "http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta";
(77 & 89 General Social Survey)

. nestreg, quietly: logit warmlt2 (yr89 male) (white age) (ed prst), robust

Block 1: yr89 male
Block 2: white age
Block 3: ed prst

+---------------------------------+
| Block | chi2 df Pr > F |
|-------+-------------------------|
| 1 | 57.18 2 0.0000 |
| 2 | 44.49 2 0.0000 |
| 3 | 22.18 2 0.0000 |
+---------------------------------+

Again, these are all Wald tests that are being conducted, not LR tests. If you try to get lr tests, the command fails, e.g.

. nestreg, lr quietly: logit warmlt2 (yr89 male) (white age) (ed prst), robust
option lrtable is not allowed with pweights, robust, or cluster()
r(198);


Personally, I am fine with a well thought out, theoretically motivated sequence of nested models. However, just manually doing the same thing that stepwise would be doing is not such a good idea.

But yes, if you are losing data like crazy because of MD, you have to be concerned about that and figure out what to do. It is easy enough to find a way to run all your analyses on the same cases, but if those cases are atypical because of MD then you have a problem.


-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME: (574)289-5227
EMAIL: Richard.A.Williams.5@ND.Edu
WWW: http://www.nd.edu/~rwilliam

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