[R-sig-ME] Specification of random effects structure

[Thorsten Aichele]

Dear List,

 

I am trying to specify the optimal random effect structure and I am not
sure, if there's a problem with my understanding of the random effects
structure, or with my data, or with none of these two.

 

Design: 

-          Two Levels, Repeated measures (L2 = 140 Participants)

-          Measure of Personality trait 'N' on L2

-          One experimental factor 'Condition' (on L1)

-          The control condition contained 12 Items. The experimental
condition contained another 12 items (Item 1-12 = control group, item 13-24=
experimental group)

-          Each participant answered all the items and all conditions. (Each
item was only answered once)

-          The experimental comparison was:  funny (experimental condition)
vs. not funny (control condition)

-          I have two random factors (participants on Level 2 and items on
level 1)

-          Items are nested under condition (as the items in both conditions
were not the same)

 

Now I want to look for a Cross-Level Interaction of the L2 variable
(personality trait 'N') with the L1 variable 'Condition' (my main
hypothesis)

 

I made the following Random effect structure under the assumption "include
every possible random slope"

                               lme(DV ~ 1 + (1 + Condition|participants) +
(1|Item)  

[I excluded random slope for N on item, as the model did not converge]

 

                               Now I tried to compare this model with a
model with fixed effects + interaction for 'condition' and 'N'

                               lme(DV ~ 1 + Condition*N + (1 +
Condition|participants) + (1|Item)  

                                               -Fixed effects part showed
nonsignificant effect for 'condition' and 'N' 

-Fixed effects part showed a significant interaction effect  Condition:N

-Fixed effect for intercept was also significant

 

Both Models share identical residual variance (1.7450). I have no idea how
this could be possible. The interaction effect is rather small (-.0247), but
I doubt, that an interaction effect could become significant without
explaining any variance.

 

I would be thankful if anyone could help me with this problem

 

Best,

Thorsten Aichele

 

 

 

 

 


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