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

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Question on interpreting interactions in linear regression models |

Date |
Tue, 15 Dec 2009 08:03:20 -0800 (PST) |

--- On Tue, 15/12/09, Padmakumar Sivadasan wrote: > I was reading a paper in which the authors are interested > in studying whether the effect of a continuous variable > (X) on a continuous outcome variable (Y) varies between > two periods (P=0 and P=1). They test this using interactions > with the following linear regression model Y=b1+b2X+b3P+b4XP > > They find that b2 is significantly different from zero, > while b4 and (b2+b4) are not significantly different from > zero. The authors conclude that X has an influence on Y in > the first period (i.e. when P=0) because b2 is significant. > Further, they state that the influence of X in the second > period (i.e. when P=1) is given by (b2+b4) and this is not > significantly different from zero, implying that X has no > influence on Y in the second period. Thus, the effect of X > on Y varies between the two periods. If the authors want to say something about the change, then he should look at the interaction effect alone. The fact that a effect is significant in one period and insignificant in another is in itself no proof that a change has occured. See for example this article from The American Statistician: http://www.stat.columbia.edu/~gelman/research/published/signif4.pdf 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/

**References**:**st: Question on interpreting interactions in linear regression models***From:*Padmakumar Sivadasan <pmathru2@illinois.edu>

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