[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
"Brooks Taggert J" <brooks.tagg@uwlax.edu> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: Proportional Odds Assumption |

Date |
Mon, 14 Dec 2009 09:02:05 -0600 |

My apologies in advance as this is not precisely a Stata question. Though I'm quite sure I can use Stata to help answer it. I'm estimating an ordered logit -ologit - in Stata, since my dependent variable is final class letter grade. I realize the strong proportional odds assumption of the model, ie parallel regressions, and test for those using Long and Freese's -brant- test. I also use Wolfe and Gould's -omodel- as well. I find that the strong assumptions are in fact too strong, and therefore turn to - slogit - proposed by Anderson (1984) to estimate different coefficients between outcome categories (actually different scaling factors for the coefficients). My question is rather simple. I want to say something about the type of mistake one makes when merely using the -ologit- results in this case. Obviously the coefficient estimates are wrong, but I can't find a paper that suggests how and in what ways they might be wrong. Kim (2003) argues that even if the test of -omodel- finds a statistically significant departure from parallel regressions it might not be practically significant. I'm wondering if anyone has a citation investigating the ways in which -ologit- might be misrepresenting the coefficient estimates. I have some sense that it is forced to use the "average" of what -slogit- might produce, but I can't confirm that. Maybe it is some weighted average? The reason is I want to be able to articulate the inappropriate inference that is made when incorrectly using the -ologit- coefficients. Clearly the precise quantity is off, but what if that is of secondary importance to the research relative to the sign and significance for the researcher when making inference? Any thoughts would be much appreciated. TJ Anderson, J. A. (1984). Regression and Ordered Categorical Variables. Journal of the Royal Statistical Society. Series B (Methodological), 46(1), 1-30. Kim, J.-H. (2003). Assessing practical significance of the proportional odds assumption. Statistics & Probability Letters, 65(3), 233-239. Taggert J. Brooks, PhD Associate Professor Department of Economics University of Wisconsin - La Crosse * * 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/

- Prev by Date:
**st: R: Choice of analysis time in survival analysis where the hazard is associated with age** - Next by Date:
**Re: st: Proportional Odds Assumption** - Previous by thread:
**st: test after iv** - Next by thread:
**Re: st: Proportional Odds Assumption** - Index(es):

© Copyright 1996–2014 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |