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RE: st: Dependent variable is a proportion


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Dependent variable is a proportion
Date   Thu, 13 May 2004 12:24:16 +0100

* top line to be sacrificed? 

Another case of "Statalist ate my top line" !

Nick 
n.j.cox@durham.ac.uk 

> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of Nick Cox
> Sent: 13 May 2004 11:58
> To: statalist@hsphsun2.harvard.edu
> Subject: RE: st: Dependent variable is a proportion
> 

Joe: Would you please spell out your objections to the 
> Gaussian family in this context? 
> 
> Nick 
> n.j.cox@durham.ac.uk 
> 
> Jhilbe@aol.com
>  
> > If you have the denominator information you can use either a 
> > grouped logistic or a poisson regression with offset. For 
> > instance, suppose that the proportion of 16 years olds taking 
> > a certain course is .30, meaning 30 students took the course 
> > at a particular site which had a total of 100 students who 
> > could have possibly taken the course. Of course, the same 
> > proportion would obtain if there were 45 students taking the 
> > course out of a possible 150. In fact, if you know the 
> > proportion AND the denominator you can calculate the numerator
> > and you've got all you need for a rate parameterization 
> > Poisson regression model (if the proportions are generally 
> > small), or grouped logistic regression (if the proportions 
> > are relatively large). 
> > 
> > Someone else may have a better idea on this, but this is my 
> > thought on it.  You do not want to use a logit link with a 
> > Gauassian family.   
>  
> > > The dependent variable I have is a proportion (percentage 
> of 16 year
> > > olds enrolled in a particular subject) which is between 0 and 86
> > > percent. I am not sure about the linear form. My dependent 
> > variable is 0
> > > only in 3,980 cases out of 112,412 sample obs. Here a zero is a
> > > structural one, because the school does not offer history 
> (which is
> > > choice subject). 
> > > 
> > > Would somebody suggest to me whether it would be better 
> to perform a
> > > logit transformation, or estimate -glm- with 
> > > family(gaussian) and
> > > link(logit). Any suggestion would be greatly appreciated!
>  
> 
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