Hi Chris,

My understanding has always been that when you include an interaction 
term you also include the constituent terms of that interaction in the 
model.  You interpret the individual terms individually (the usual "one 
unit change, holding all else at its mean" thing).  The interaction you 
need to graph out.  Wherever your confidence intervals cross zero, the 
interaction is insignificant.

We had a thread on how to do so just yesterday (and i started it).  You 
might also take a look at Brambor, Clark, and Golder (2006) in 
/Political Analysis /for the mechanics behind it.

Shane
On 01/29/2013 08:27 PM, Christopher Kurby wrote:
> Hello everyone,
>
> I have a mixed effect model in which I am modeling an interaction between two continuous variables. The interaction between them is significant. Can anyone recommend a method to tease apart this interaction? For standard multiple regression, one would compute simple slopes from the regression equation. Is the same done for mixed effect models? Just for the sake of argument, let's say my model is below. In case it is important to know, I also have some covariates in the model, but I'm modeling only the main effect of the covariate. Again, I am interested in teasing apart the interaction between the two continuous predictors:
>
> lmer(DV ~ var1 * var2 + cov1 + (1|Subject) + (1|item), data=dat)
>
> Thank you in advance.
>
> Chris
>
> Christopher A. Kurby
> Assistant Professor of Psychology
> Grand Valley State University
> Allendale, MI 49401
> phone: 616-331-2418
> email: kurbyc@gvsu.edu<mailto:kurbyc@gvsu.edu>
>
>
>
>
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>
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-- 
Shane Gleason
Doctoral Candidate
Southern Illinois University:Carbondale
Department of Political Science
Faner 3172


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