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RE: st: gologit2

From   David Jacobs <>
Subject   RE: st: gologit2
Date   Fri, 18 Apr 2008 11:24:44 -0400

Thanks so much for this. And it should be helpful to others with similar problems.

In answer to your question, the number of ranks is somewhere between 25 and 30 because we've combined several ordinal scales and gotten a decent alpha. But I'm working on other projects now as the final data set for the ordinal logit or probit analysis is not yet ready. I'll have to get back to you later to give an exact answer your question.

I looked for the number of ranks limit for slogit but couldn't find anything on this in the manual. I suspect it is well below the 50 limit for the ordinal estimators.

But maybe that's not the reason slogit won't run. I find that estimation routine often appears to be extremely sensitive to what seem to be minor data problems. When I can get started on this project again, I probably should email Stata to discover what the slogit limit is.

Thanks again and thanks agian to Marteen.

Dave Jacobs

At 11:53 PM 4/17/2008, you wrote:

At 08:14 PM 4/17/2008, David Jacobs wrote:
A student and I have about 1300 U.S. state-years in a pooled time series analysis of a state legal outcome that is measured as an ordinal scale (I plan to cluster on the state IDs to adjust for the pooled nature of the data or to use the pooled ordinal estimators in Limdep if I have to).

I understand, of course, how to use the BIC test to compare models, but I don't understand how this test can be used to test for the absence of proportionality in an ordinal logit of probit analysis.
Here is an example. You need gologit2, available from SSC:

. use "";
(77 & 89 General Social Survey)

. quietly ologit warm yr89 male white age ed prst

. est store proportional

. quietly gologit2 warm yr89 male white age ed prst

. est store nonproportional

. lrtest proportional nonproportional, stats force

Likelihood-ratio test LR chi2(12) = 49.20
(Assumption: proportional nested in nonproportio~l) Prob > chi2 = 0.0000

Model | Obs ll(null) ll(model) df AIC BIC
proportional | 2293 -2995.77 -2844.912 9 5707.825 5759.463
nonproport~l | 2293 -2995.77 -2820.311 21 5682.622 5803.112
Note: N=Obs used in calculating BIC; see [R] BIC note

The likelihood ratio test says to reject proportional odds. The BIC test likes proportional odds better. I guess that makes the AIC test the tiebreaker, and it likes nonproportional odds better. If you use gologit2's -autofit- option, you can find an intermediate model that fits best of all. For more on gologit2, see

By the way, I can't get slogit to work at all (the Stata rountine won't give estimates) perhaps (?) because we have too many ranked outcomes in this dependent variable.
How many outcomes do you have? In ologit, the limit is 50; I don't know about slogit. If you provide some output we might be able to make a better guess.

Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME: (574)289-5227
EMAIL: Richard.A.Williams.5@ND.Edu

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