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From |
Sunniva Andersen <andersensunniva6@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: comparing logistic regression coefficients between samples |

Date |
Wed, 6 Jul 2011 11:24:45 +0200 |

Thank you Maarten. Sunniva 2011/7/6 Maarten Buis <maartenlbuis@gmail.com>: > On Tue, Jul 5, 2011 at 11:31 PM, Sunniva Andersen wrote: >> How can I do this in stata 11. I have estimated the same >> logistic regression model in two different samples, A and B. >> I compute AME after the logit. Can someone suggest a method by which I >> can test the hypothesis that var1 (b1) in sample A is different from >> var1(b1) in sample B. > > That is an interaction effect, and those are hard when you want to do > a logistic regression and marginal effects. In essence they will be so > different from observation to observation that your conclusion will be > that for some persons var1 will have a significantly larger effect in > sample A than B, for others var1 will have a significantly smaller > effect in sample A than B, and for the remaining individuals there is > no significant difference. If this variability in interaction effect > across individuals represented something present in the data, than > there could be situations where this might be useful. However, it is > just the result of the fact that marginal effects fit a linear model > on top of your logit model in order to summarize your logit model, and > that linear model does not fit very well. Averaging is not going to > solve it as these effects are just too different from one another in > order to be meaningfully summarized with one number. > > You have two options: > 1) If you want to continue using logistic regression, you'll have to > interpret your results as odds ratios and the interaction term as a > ratio of odds ratios. See: M.L. Buis (2010) "Stata tip 87: > Interpretation of interactions in non-linear models", The Stata > Journal, 10(2), pp. 305-308. > > 2) If you want to continue interpreting your coefficients as a > difference in probabilities rather than ratios of odds than you can > use a linear probability model (i.e. just use -regress- in combination > with the -vce(robust)- option instead of -logit-). > > 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/ > * * 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**:**st: comparing logistic regression coefficients between samples***From:*Sunniva Andersen <andersensunniva6@gmail.com>

**Re: st: comparing logistic regression coefficients between samples***From:*Maarten Buis <maartenlbuis@gmail.com>

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