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Re: st: interpreting negative and positive AIC- OLS VS. GLM

From   Maarten buis <>
Subject   Re: st: interpreting negative and positive AIC- OLS VS. GLM
Date   Thu, 19 Aug 2010 17:12:00 +0000 (GMT)

--- On Thu, 19/8/10, Arina Viseth wrote:
> From your experience do you have a recommendation for
> assessing  model fit for this kind of model?

The thing that realy bites when it comes to modeling 
proportions with a linear model are the boundaries: 
You cannot have a linear line that will respect these 
boundaries forever (unless you have a horizontal line). 
So at some point a linear effect will have to become
nonlinear. The question is does that happen within
the range of your data, or can a linear line reasonably
represent your data. 

The first thing I would do is just plot the distribution
of your proportion and see if it gets close to one or
both of the boundaries. If that is the case I would not
use a linear model, and instead move towards one of the
alternatives like a fractional logit model or -betafit-
(which you can download by typing in Stata -ssc install
betafit-). A nice tool for that is Nick Cox's -stripplot-
(to install type in Stata -ssc install stripplot-), like
in the example below:

*------------------------- begin example -------------------
use, clear
stripplot governing, stack width(.01)
*------------------------- end example ---------------------

If most of your observations are somewhere in the middle
and you thus think that linear regression is ok for your 
data, I would still check the residuals to see if the 
linear effects are appropriate and whether the boundaries 
haven't introduced more heteroskedasticity then you feel 
comfortable with.

Hope this helps,

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


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