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Re: st: compare effect size between dummys and metrics variables in logistic regression


From   Maarten buis <maartenbuis@yahoo.co.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: compare effect size between dummys and metrics variables in logistic regression
Date   Mon, 27 Sep 2010 11:48:56 +0000 (GMT)

--- On Mon, 27/9/10, Morten Hesse wrote:
> It may well be that Michael is right. But if I conduct a logstic
> regression, and then write:
> 
> test [continuous covariate]=[1.dichomotomous covariate]
> 
> Then I get a chi-square statistic (with one df) and a
> p-value. It looks just like any other result of "test":
> 
> In my understanding, this asks the question "is the
> difference between the value of the coefficient when
> dic_covar=1 significantly different from the coefficient of
> cont_covar.
> 
> If this does not make sense, then why does STATA do it? Is
> this not what Joerg would want to use?

It is a substantive question. Stata knows nothing about 
substantives, that is what we researchers get paid for. Stata
is only for the boring mechanical part of an analysis.

Consider the following example:

*----------- begin example ------------
sysuse nlsw88, clear
gen black = race == 2 if race <= 2
logit union i.black grade
test 1.black = grade
*------------ end example -------------

The coeficient for 1.black compares the odds of union membership
of white respondents with the odds of union membership of black
respondents.

The coeficient of grade compares the odds of membership of a 
respondent with the odds of membership of another respondent with
1 more year of education.

Can you compare the difference between black and white with the
difference between x years of schooling and x + 1 years of schooling?
Probably not, but how is Stata supposed to know? Like any other
computer program, Stata just does what you tell it to do. It is up
to you to make sure that what you tell it to do makes sense.

A valid position  when it comes to comparing the effects of the 
kind of variables I used in the example above is that such variables 
are just different, and you just cannot compare them. 

Sometimes you have good reason to want to compare the effects of such 
variables (often that is not the case, and than you should just not 
do it), and that is where standardization comes in. Standardization 
is intended to make the unit of variables comparable. There are various 
ways of doing that 
<http://www.stata.com/statalist/archive/2010-09/msg01258.html>. Which
one you think is least awkward is a substantive question that depends
on the specific variables you are comparing.

Hope this helps,
Maarten

--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany

http://www.maartenbuis.nl
--------------------------


      

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