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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. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: St: Ordered Logit Question***From:*"Jason Dean, Mr" <jason.dean@mail.mcgill.ca>

**Re: st: St: Ordered Logit Question***From:*Clive Nicholas <clivelists@googlemail.com>

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