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From | Maarten buis <maartenbuis@yahoo.co.uk> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: main effect insignificant, interaction term significant |
Date | Wed, 16 Feb 2011 17:21:39 +0000 (GMT) |
--- On Wed, 16/2/11, Gáti Annamária wrote: > Do (and if so, how do) we interpret interaction terms in > the following regression example: > > we want to explain whether someone got lung cancer or not > and we explain this by gender and smoking. > > gender= non sign. > ever smoked= non sign. > gender*smoked= sign One strategy is to reformulate your model so that you directly see the effect of ever_smoked for men and for women. Consider the example below, which I think is similar to your problem (black and white would be male and female in your problem; collgrad would be ever_smoked in your problem; and union membership would be equivalent to lung cancer in your problem). In the group white women without college degree you would expect .24 union members for every non-union member (the baseline odds). This odds is 1.56 times higher for black women. The odds of unionmembership increases by a factor 1.89 when black women get a college degree and by a factor 1.70 when white women get a college degree. In the example below the difference in these effects are non- significant, but in your case that difference will be significant. *-------------------- begin example --------------------- sysuse nlsw88, clear drop if race == 3 // drop "others" // I think that it is informative to make the // interaction terms yourself: // race would be gender in your model // you would make the variables male and female instead gen byte black = race == 2 if !missing(race) gen byte white = race == 1 if !missing(race) // collgrad would be never_smoked in your model gen byte blackXcoll = black*collgrad gen byte whiteXcoll = white*collgrad gen byte baseline = 1 logit union black blackXcoll whiteXcoll baseline, nocons or // if we want to test whether the effect of // collgrad is the same for black and white women // we can either estimate your model and look at // the significance of the interaction term or: test blackXcoll = whiteXcoll // However, you can also use Stata's new factor // variable notation: logit union i.race i.race#i.collgrad baseline, nocons or *--------------------- end example ---------------------------- (For more on examples I sent to the Statalist see: http://www.maartenbuis.nl/example_faq ) 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/