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AW: st: St: Ordered Logit Question


From   "Martin Weiss" <martin.weiss1@gmx.de>
To   <statalist@hsphsun2.harvard.edu>
Subject   AW: st: St: Ordered Logit Question
Date   Thu, 7 May 2009 09:20:03 +0200

<> 

"Hamilton (2004: 278-80) has some concise stuff on interpreting the
thresholds (although my copy is old)..."



The relevant pages in Hamilton (2009),
http://www.stata.com/bookstore/sws.html, 
are 293-295.


HTH
Martin

-----Ursprüngliche Nachricht-----
Von: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Clive Nicholas
Gesendet: Donnerstag, 7. Mai 2009 05:20
An: statalist@hsphsun2.harvard.edu
Betreff: Re: st: St: Ordered Logit Question

Jason Dean wrote:

> I am running two ordered logits equations. One for immigrants and one for
the native-born. > Each has the exact same independent and dependent
variables. There are 3 categories for > the depedent variable. I find that
the threshhold parameters are quite different for these two > groups.
Specifically, both cutpoints are much lower for immigrants. Can anyone
enlighten > me as to how I should interpret this? To me this means, all else
equal, immigrants are
> much more likely to be in the highest category and much less likely to be
in the lowest
> category. Can I just interpret this in a similar manor as if these two
groups had different
> intercepts in a linear regression? Also, is it appropriate to compare
marginal effects
> between immigrants and the native-born.

My first reaction to this would be to run the one model only for all
of your cases, if all of your variables are the same in both models,
including a dummy variable for ethnic origin (say: 0=non-native;
1=native). Then you only have to interpret one set of thresholds.
Running -predict- after -ologit- will give you the estimated scores on
Y* (the latent construct of your dependent variable whose values are
measured continuously) against which you can compare the thresholds.

Hamilton (2004: 278-80) has some concise stuff on interpreting the
thresholds (although my copy is old), whilst Jaccard (2001: 17)
explains why it really isn't a good idea to run seperate logistic
regressions for discrete groups.

-- 
Clive Nicholas

[Please DO NOT mail me personally here, but at
<clivenicholas@hotmail.com>. Please respond to contributions I make in
a list thread here. Thanks!]

Hamilton LC (2004) "Statistics With Stata 8", Belmont, CA: Thomson.
Jaccard (2001) "Interaction Effect In Logistic Regression", QASS
Series Paper 135,
 Thousand Oaks, CA: Sage.

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