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From |
"Lee, Albert" <Albert.Lee@hud.gov> |

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

Subject |
st: RE: Treatment of outcome categories in Ologit |

Date |
Mon, 28 Sep 2009 17:29:43 -0400 |

You may only want to do an ologit if the numeric value represent ordered categories, and the distance between these categories does not matter. For example, 3.6 is preferred to 3.5; and 4.2 is preferred to 3.6. However, the degree of preference in the latter case is not six time higher than in the former case. If orders matter, and not the degree from one category to another, then you can group your score into categories, i.e., egen ord_cat_score=group(score) The ord_cat_score will recode your scores in order into 1, 2, 3, etc... regardless of cutoff. You can run an ologit on the ord_cat_score. I suggest you look into Long and Freese, who has a nice treatment on ologit. http://www.stata.com/bookstore/regmodcdvs.html If the order and the difference between the score matter, then you may want to consider a fractional logit. To do that you may want to rescale your scores from 0 to 1 by dividing your scores by 50, i.e., 0.07=3.5/50. Fractional logit is a better option than OLS for the reasons you mentioned below. This link provides some details: http://www.stata.com/statalist/archive/2005-10/msg00883.html I hope it helps. Albert. -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Rijo John Sent: Monday, September 28, 2009 4:56 PM To: stata Subject: st: Treatment of outcome categories in Ologit Hi statalist, I have a dependent variable (a score) which is ordinal in character. However, it has around 30 or so categories the value of which are continuous ranging anywhere from 0 to 50. There could be values like 3.5, 3.6, 4.2 etc. My questions are: 1) Is Ologit the right estimation method for this type of decision variables? ( I should say that I am not particularly interested in analyzing each cutpoints. I am only interested in how the changes in explanatory variables affect the over all score. I dont expect OLS to return good results because the dep var is both bounded and ordinal in character. 2) Given that ologit is the right method, how does stata treat the values in my score variable while estimating? Does it round off the scores into whole numbers before estimating or does it treat them as they are. If it rounds off obviously 3.5 score and 3.6 score will be treated same. Stata description on it tells me "The actual values taken on by the dependent variable are irrelevant, except that larger values are assumed to correspond to "higher" outcomes." My estimation gives me as many coefficients for cut-points as there are categories, which makes me assume that it must not be rounding those numbers. Can someone clarify? Thanks. * * 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/

**Follow-Ups**:**Re: st: RE: Treatment of outcome categories in Ologit***From:*Rijo John <rmjohn@gmail.com>

**References**:**st: Treatment of outcome categories in Ologit***From:*Rijo John <rmjohn@gmail.com>

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