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From |
Richard Williams <Richard.A.Williams.5@ND.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
RE: st: gologit2 |

Date |
Mon, 14 Apr 2008 16:20:59 -0500 |

At 11:54 AM 4/14/2008, Verkuilen, Jay wrote:

My experience is that it is rare to have a model where the proportional odds assumption isn't violated! Often, though, the violation only involves a small subset of the variables, in which case gologit2 can be useful. You might also want to consider more stringent alpha levels (e.g. .01, .001) to reduce the possibility of capitalizing on chance. You can also try to assess the practical significance of violations, e.g. do my conclusions and/or predicted probabilities really change that much if I stick with the model whose assumptions are violated as opposed to a (possibly much harder to understand and interpret) model whose assumptions are not violated.Maarten buis wrote: >>If you are unsure, than go through the logic of testing: formulate the null-hypothesis. <snip> << The one addendum I would add is this: If the formal test says reject the null but the resulting violation is "small", you may want to think twice about tossing out the proportional odds assumption. Such violations are often found by capitalizing on chance and wouldn't replicate (instead you'll find some other violation elsewhere). It may be worth it to assess these kinds of assumptions on a calibration sample and have a randomly selected holdout sample for later validation of your model.

Finally, while I happen to like gologit2, there are a lot of other categorical and ordinal models out there that might be worth a look depending on the problem.

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Richard Williams, Notre Dame Dept of Sociology

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**Follow-Ups**:**RE: st: gologit2***From:*"Verkuilen, Jay" <JVerkuilen@gc.cuny.edu>

**References**:**Re: st: gologit2***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**RE: st: gologit2***From:*"Verkuilen, Jay" <JVerkuilen@gc.cuny.edu>

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