[R-sig-ME] Specification of random effects structure
thor@ten@@|che|e @end|ng |rom un|-wuerzburg@de
Thu Apr 18 16:59:17 CEST 2019
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.
- 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=
- 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
- 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
I made the following Random effect structure under the assumption "include
every possible random slope"
lme(DV ~ 1 + (1 + Condition|participants) +
[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
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