Hi Clive, sorry I forgot to ask you for the title of the book you gave me - Jaccard?
Thanks again.
________________________________________
From: [email protected] [[email protected]] On Behalf Of Clive Nicholas [[email protected]]
Sent: Wednesday, May 06, 2009 11:19 PM
To: [email protected]
Subject: 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
<[email protected]>. 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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