In my liner mixed effect model, there are two independent variables location
(2 factors: E, word) and cond_aud
(3 factors:CA,EA,NoA). As I used sliding contrast which means that the model summary only gave me the interactions of location: CA-EA
, location: EA-NoA
. To test the interaction of location: NoA-CA
, I used emmeans function without adjustment:
contrast(emmeans(lmerwp2,~location*cond_aud),interaction=c("pairwise","pairwise"),adjust="none")
And results show a significant interaction between E-words
and CA -NoA
as below:
However, follow-up pairwise comparisons show that there is no simple effect of location
(i.e., E-words) in CA
or NoA
(see below), although the direction of effects are opposite. How could the interaction be significant when no simple effects are significant? Or should I just report the different directions of simple effects?
emmeans
? The outputs you show say "results are averaged over the levels of: cond_vd," a predictor not in your calls toemmeans
. I'm not sure that will matter for an answer to your question, but it might be informative in providing a context. $\endgroup$dv ~ location * cond_vd * cond_aud + (cond_vd + cond_aud | pp) + (1 | worditem)
showed that there is no significant interaction oflocation:cond_vd:cond_aud (CA-NoA)
, which means that the interaction oflocation: cond_aud (CA-NoA)
is not modulated by the variable ofcond_vd
. So in this case, based on the reports I put in the post, I should just report that the effect of location was in different directions inCA
andNoA
. Did I understood it accurately? $\endgroup$