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From |
Clive Nicholas <clivelists@googlemail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: interaction in linear regression |

Date |
Mon, 28 Sep 2009 01:26:39 +0100 |

Lana Carneiro Almeida wrote: > I want to test for interaction in linear regression, between two > categorical variables, and I used the following comand in STATA: > > xi: regress y i.var1 i.var2 var3 i.var1*i.var2 > > so the model gives me more than one beta/p-values of interaction, but > ommit one category, and I would like to know the p-value of all beta. > How can I get it? Any single first-order variable can only be interpreted when the other first-order variables are zero, which may or may not be meaningful to you, and in any case are only included so that you obtain the best-possible estimates on the coefficients of your interaction terms (otherwise, you are assuming that these terms equal zero: almost always an incorrect assumption). If you are looking to fit two interaction terms (e.g., -d1*d2- and -d1*d3-), then -d2*d3-, much like -d1-, is redundant, as this will be defined by the other two interaction terms. > Another question is about interpretation of interaction: if only one > beta is statisticaly significant, can I interpretate that there are > interaction between the two variables? As said before, the interaction is the _main_ action. If the coefficient on this term is insignificant, then you have insufficient evidence that, say, the effect of -d1- on -y- varies by values of -d2-. Unless you have other interactions to test, you would then revert to your previous model _without_ the interaction term. -- Clive Nicholas [Please DO NOT mail me personally here, but at <clivenicholas@hotmail.com>. Please respond to contributions I make in a list thread here. Thanks!] "My colleagues in the social sciences talk a great deal about methodology. I prefer to call it style." -- Freeman J. Dyson. * * 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: interaction in linear regression***From:*lana@usp.br

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