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From |
Rosie Chen <jiarongchen2002@yahoo.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Odds ratio |

Date |
Fri, 9 Apr 2010 06:52:25 -0700 (PDT) |

Thank you all for the thoughtful responses to my inquiry. Sorry that I did not realize that calculation of probabilities in HGLM and regular one-level logistic model could be different. I guess HLM will complicate this issue further.... Rosie ----- Original Message ---- From: Richard Williams <Richard.A.Williams.5@ND.edu> To: "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>; "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> Sent: Fri, April 9, 2010 8:12:29 AM Subject: Re: st: Odds ratio Notre Dame insists that I take valuable time away from Statalist and waste it on teaching classes and the like, so just a few quick comments/questions: At 02:14 AM 4/9/2010, Maarten buis wrote: > --- On Fri, 9/4/10, Rosie Chen wrote: > > I am doing HLM analysis, so it is impossible to use the Stata > > syntaxt to calculate the predicted probability. So I will > > just do the calculation by myself in excel. Here is what I > > plan to do: I will calculate log-odds and then convert > > them into predicted probabilities for individuals with > > characteristics that I am interested in so as to demonstrate > > the magnitude of the effect for a specific variable. > > Sorry for being blunt but that is a very bad idea. There are > very good reasons why Stata isn't giving you those probabilities > directly: These multilevel models take into account group level > variation, while your approach doesn't. HLM was a new wrinkle introduced in Rosie's last email. Just to be clear, Maarten, is your criticism specific to HLM models -- i.e. the calculations will be wrong in such cases -- or is it more general than that? I don't do HLM models so I don't know what new complications they introduce. > > For example, in order to explain the gender difference in the > > probability of an outcome, I will compute the difference in > > the predicted probability between females and males > > The key issue with odds ratios is that I would like to have the > baseline odds present, to help me interpret the odds ratio (which > in a sense helps to bridge the gap between absolute and relative > effects). The problem is that by default Stata suppresses those. > The trick is to add a variable baseline, which is always one, and > add the -noconstant- option. This trick is discussed in the paper > I refered to before, and I learned it from: Roger Newson (2003), > Stata tip 1: The eform() option of regress. The Stata Journal, > 3(4): 445. <http://www.stata-journal.com/article.html?article=st0054> I agree that the odds ratios become much more useful when you have the baseline odds, although I would still prefer to convert to probabilities. But, you still have to decide on the baseline. Exponentiating the constant gives you the odds for a person who has a score of 0 on every independent variable. If, say, every variable has been centered to have a mean of 0, this may be a good baseline, i.e. you would then be getting the odds for an "average" person. But it is not a good baseline if 0 is not a meaningful value for every variable, e.g. I wouldn't want to use as my baseline somebody who was 0 years old, weighed 0 pounds, and got a score of 0 on a test where the lowest possible score is 400. With what I proposed before, you would try different baselines, e.g. you might compute the probabilities for an "average" male and then compute the probabilities for an otherwise-identical "average" female. You could also do the same for above average and below average males and females. > I am slowely getting used to odds, so the distinction between odds > and probabilities doesn't bother me any more: You can quantify the > likelihood of an event by computing the expected number of success > per 100 trials (100*probability) or by the expected number of > success per failure (the odds). Just don't mix the two up, as > sometimes happens when people try to interpret odds ratios as risk > ratios. True, but I just had a student who couldn't tell me what the probability of success was if the odds were 3 to 1 in your favor. He said he'd always wondered what that meant. :) Odds aren't that hard to understand but I think probabilities are still easier for most people. ------------------------------------------- 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 WWW: http://www.nd.edu/~rwilliam * * 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**:**Re: st: Odds ratio***From:*Rosie Chen <jiarongchen2002@yahoo.com>

**Re: st: Odds ratio***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**Re: st: Odds ratio***From:*Richard Williams <Richard.A.Williams.5@ND.edu>

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