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RE: st: Interaction terms in a logit model


From   "Daniel Schneider" <daniel.schneider@stanford.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Interaction terms in a logit model
Date   Sat, 19 Mar 2005 12:20:56 -0800

I am using it to test the moderating effect of motivations of media use
on the impact of media use on a dependent variable. So, no, I don't
think I am testing for group differences (like the Chow test in linear
models would). In fact, while I used a dummy variable in my example, I
am interacting two continuous variables, but I wanted to keep the
example simple.

(but thanks for the link anyway, I am interested in that for different
reasons)

Daniel

> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu 
> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of 
> Hoetker, Glenn
> Sent: Saturday, March 19, 2005 10:13 AM
> To: statalist@hsphsun2.harvard.edu
> Subject: RE: st: Interaction terms in a logit model
> 
> 
> If your dummy variable does in fact reflect that you are 
> looking at differences across groups, you really need to read 
> Allison's 1999 piece cited below. Partially building on 
> Allison's piece, I have a paper in which I test whether 
> cross-group differences in residual variation really matter 
> (as opposed to being a theoretical concern without practical 
> impact). Monte Carlo simulations indicate that even small 
> differences in residual variation can indeed invalidate 
> cross-group comparisons. Using interaction terms to model 
> different groups in logit models ends up being particularly 
> risky--you can even end up with significant results in the 
> opposite direction!  Allison's tests, while they have some 
> limitations, are a definite improvement on common practice. 
> 
> If you are interested, the paper is at 
> http://www.business.uiuc.edu/ghoetker/documents/Hoetker_comp_l
> ogit.pdf.
> You can install the Stata code it discusses from within Stata: 
> 
> 	net from http://www.business.uiuc.edu/ghoetker
> 
> and carry on as normal from there.  Please be aware that the 
> software is work in progress.  In particular, there is 
> absolutely no sanity checking.
> 
> As far as I know, so long as you aren't using the interaction 
> term to model cross-group differences (which is what 
> introduces the potential for differences in residual 
> variation), you should be okay.
> 
> Glenn
> 
> Glenn Hoetker
> Assistant Profess of Strategy
> College of Business
> University of Illinois at Urbana-Champaign
> ghoetker@uiuc.edu
> 
> 
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of 
> Richard Williams
> Sent: Saturday, March 19, 2005 11:51 AM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: Interaction terms in a logit model
> 
> At 10:00 PM 3/18/2005 -0800, Daniel Schneider wrote:
> >Dear List,
> >
> >I have read the articles by Norton, Wang, Ai (2004) as well as their 
> >more theoretical paper (Ai & Norton (2000)) and I am aware of other 
> >literature describing the same problem. I think I understood the 
> >theoretical problems and reasoning behind their approach, but 
> >unfortunately I really have a hard time of really 
> understanding what I 
> >have to do when I use interaction terms in a logit regression.
> 
> Daniel, could you provide more precise citations for the 
> articles you are 
> mentioning?  I'd be curious to read more about what they say.
> 
> Not having read these papers, I don't know specifically what 
> your concern 
> is, but Paul Allison's "Comparing Logit and Probit 
> Coefficients Across 
> Groups," SOCIOLOGICAL METHODS & RESEARCH, Vol. 28 No. 2, 
> November 1999 
> 186-208, may be worth a look.  Here is the abstract:
> 
> "In logit and probit regression analysis, a common practice 
> is to estimate 
> separate models for two or more groups and then compare 
> coefficients across 
> groups. An equivalent method is to test for interactions 
> between particular 
> predictors and dummy (indicator) variables representing the 
> groups. Both
> 
> methods may lead to invalid conclusions if residual variation differs 
> across groups. New tests are proposed that adjust for unequal 
> residual 
> variation."
> 
> 
> -------------------------------------------
> Richard Williams, Notre Dame Dept of Sociology
> OFFICE: (574)631-6668, (574)631-6463
> FAX:    (574)288-4373
> HOME:   (574)289-5227
> EMAIL:  Richard.A.Williams.5@ND.Edu
> WWW (personal):    http://www.nd.edu/~rwilliam
> WWW (department):    http://www.nd.edu/~soc
> 
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