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From |
Maarten buis <maartenbuis@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: compare effect size between dummys and metrics variables in logistic regression |

Date |
Mon, 27 Sep 2010 11:48:56 +0000 (GMT) |

--- On Mon, 27/9/10, Morten Hesse wrote: > It may well be that Michael is right. But if I conduct a logstic > regression, and then write: > > test [continuous covariate]=[1.dichomotomous covariate] > > Then I get a chi-square statistic (with one df) and a > p-value. It looks just like any other result of "test": > > In my understanding, this asks the question "is the > difference between the value of the coefficient when > dic_covar=1 significantly different from the coefficient of > cont_covar. > > If this does not make sense, then why does STATA do it? Is > this not what Joerg would want to use? It is a substantive question. Stata knows nothing about substantives, that is what we researchers get paid for. Stata is only for the boring mechanical part of an analysis. Consider the following example: *----------- begin example ------------ sysuse nlsw88, clear gen black = race == 2 if race <= 2 logit union i.black grade test 1.black = grade *------------ end example ------------- The coeficient for 1.black compares the odds of union membership of white respondents with the odds of union membership of black respondents. The coeficient of grade compares the odds of membership of a respondent with the odds of membership of another respondent with 1 more year of education. Can you compare the difference between black and white with the difference between x years of schooling and x + 1 years of schooling? Probably not, but how is Stata supposed to know? Like any other computer program, Stata just does what you tell it to do. It is up to you to make sure that what you tell it to do makes sense. A valid position when it comes to comparing the effects of the kind of variables I used in the example above is that such variables are just different, and you just cannot compare them. Sometimes you have good reason to want to compare the effects of such variables (often that is not the case, and than you should just not do it), and that is where standardization comes in. Standardization is intended to make the unit of variables comparable. There are various ways of doing that <http://www.stata.com/statalist/archive/2010-09/msg01258.html>. Which one you think is least awkward is a substantive question that depends on the specific variables you are comparing. Hope this helps, Maarten -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * 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: compare effect size between dummys and metrics variables in logistic regression***From:*Morten Hesse <mh@crf.au.dk>

**References**:**Re: st: compare effect size between dummys and metrics variables in logistic regression***From:*Morten Hesse <mh@crf.au.dk>

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