Hi, you can include interation terms for the explanitory variables but not for the cut point (ancillary) paramaters. I find these to be much different between groups.
________________________________________
From: [email protected] [[email protected]] On Behalf Of Michael I. Lichter [[email protected]]
Sent: Friday, May 08, 2009 3:33 PM
To: [email protected]
Subject: Re: st: St: Ordered Logit Question
Why can't you include an interaction term? -ml
Jason Dean, Mr wrote:
> Hi Clive, the reason why I don't pool both groups together is that the estimated cut point point paramters are much different for immigrants and the native-born. And you cannot interact them with an immigrant dummy in the ologit command. Thus it constrains them to be the same across groups. I find it makes a large difference, in terms of the results, between pooling them and running them separate. Is my thinking on this wrong?
>
> ________________________________________
> 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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--
Michael I. Lichter, Ph.D. <[email protected]>
Research Assistant Professor & NRSA Fellow
UB Department of Family Medicine / Primary Care Research Institute
UB Clinical Center, 462 Grider Street, Buffalo, NY 14215
Office: CC 125 / Phone: 716-898-4751 / FAX: 716-898-3536
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