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From | Richard Williams <richardwilliams.ndu@gmail.com> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: Re: Why main effects are significant but interction term is not signficant |
Date | Tue, 08 Mar 2011 15:21:28 -0500 |
At 07:49 AM 3/8/2011, Maarten buis wrote:
--- On Tue, 8/3/11, Mike wrote: > y= beta+beta1*x1+beta2*x2+beta3*x1*x2+epsilon > > You can think of y as income, x1 is gender (1 for male) and > x2 is the educational level. > > The OLS gives a significant results for beta1 and beta2 > but not beta3. In the context of the example, male and higher > education help having higher income. But the interaction of > male and higher education doesn't have any significant effect > on income. That is possible, and in smaller to medium size studies even likely. Interaction effects typically require very large datasets before they become significant. You can see that by looking at the absolute minimum number of observations necessary to get an estimate:
Also, my suspicion (possibly wrong) is that interaction effects will tend to be smaller than main effects and hence harder to detect in a sample. For example, I can envision a world in which the effect of X on Y is 5 for men and 6 for women. In a reasonably large sample, it won't be too hard to detect that, for both groups, the effect of X differs quite a bit from zero. But, it will be much more difficult to determine that there is a significant difference between the effects in the 2 populations. Put another way, it takes a larger N to detect small effects than it does to detect large effects.
------------------------------------------- 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/