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Re: st: Treatment of outcome categories in Ologit


From   Maarten buis <maartenbuis@yahoo.co.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Treatment of outcome categories in Ologit
Date   Mon, 28 Sep 2009 21:46:38 +0000 (GMT)

--- On Mon, 28/9/09, Rijo John wrote:
> I have a dependent variable (a score) which is ordinal in
> character. However, it has around 30 or so categories the
> value of which are continuous ranging anywhere from 0 to
> 50. There could be values like 3.5, 3.6, 4.2 etc. My
> questions are:
> 
> 1) Is Ologit the right estimation method for this type of
> decision variables? ( I should say that I am not
> particularly interested in analyzing each cutpoints. I am
> only interested in how the changes in explanatory variables
> affect the over all score. I dont expect OLS to return good
> results because the dep var is both bounded and ordinal in
> character.

You will have to decide what "overall score" means in your 
case. Usually, if you have that many categories it does make 
some sense to define that as the mean, in which case OLS is
a good place to start, especially as it is usually much more
robust than the alternatives. If you want to be more puritan, 
you can look at how the median changes over explanatory 
variables using quantile regression (-qreg-). However, since
you have that many categories, I'd be inclined not to use 
-ologit-.

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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