Hi all,
I have two questions, one regarding the testing of random slopes in
three-level models and one regarding an error message:
My data have a three-level structure: Items (level 1, n=6111) are nested
within measurement occasions (level 2, n=2111), and measurement
occasions are nested within persons (level 3, n=84). I assume a
curvilinear relationship between one predictor and one outcome (both at
level 1), so I included a linear and a squared term as predictors in my
model. I am mainly interested in the fixed effects but to find out the
"best" model to report, I want to test whether random slopes are needed
at level 2 and/or level 3.
I wonder whether there is any "best practice" in which order the random
slopes should be tested in three-level models?
I tried to compute a full model (random slopes for all terms at both
levels) to compare this with models in which only one of the four random
terms was fixed (this was done for all four possible random slopes).
Using lme4, I got an error message with regard to the full model :
mod.full <- lmer(OUTCOME ~ 1 + PRED.linear + PRED.squared + (1 +
PRED.linear + PRED.squared| ID.L2) + (1 +
PRED.linear + PRED.squared | ID.L3), data)
Error: number of observations (=6111) <= number of random effects
(=6333) for term (1 + PRED.linear + PRED.squared | ID.L2); the
random-effects parameters and the residual variance (or scale parameter)
are probably unidentifiable
If more information is needed, please let me know.
Thanks,
Charlotte
--
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Charlotte Arndt
Department of Psychology
University of Koblenz-Landau
Fortstr. 7
76829 Landau, Germany
E-Mail: arndtch at uni-landau.de
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