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


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
--------------------------


      

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