Hello. I am new to R and new to linear mixed effects modeling. I am trying to
model some data which has two factors. Each factor has three levels rather
than continuous data. Specifically, we measured speech at Test 1, Test 2 and
Test 3. We also had three groups of subjects: RepTP, RepNTP and NoRepNTP.
I am having a really hard time interpreting this data since all the examples I
have seen in the book I am using (Baayen, 2008) either have continuous
variables or factors with only two levels. What I find particularly confusing
are the interaction terms in the output. The output doesn't present the full
interaction (3 X 3) as I would expect with an ANOVA. Instead, it only presents
an interaction term for one Test and one Group, presumably comparing it to the
reference Test and reference Group. Therefore, it is hard to know what to do
with the interactions that aren't significant. In the book, non-significant
interactions are dropped from the model. However, in my model, I'm only ever
seeing the 2 X 2 interactions, not the full 3 X 3 interaction, so it's not
clear what I should do when only two levels of group and two levels of test
interact but the third group doesn't.
If anyone can assist me in interpreting the output, I would really appreciate
it. I may be trying to interpret it too much like an ANOVA where you would be
looking for main effects of Test (was there improvement from Test 1 to Test 2),
main effects of Group (was one of the Groups better than the other) and the
interactions of the two factors (did one Group improve more than another Group
from Test 1 to Test 2, for example). I guess another question to pose here is,
is it pointless to do an LME analysis with more than two levels of a factor?
Is it too much like trying to do an ANOVA? Alternatively, it's possible that
what I'm doing is acceptable, I'm just not able to interpret it correctly.
I have provided output from my model to hopefully illustrate my question. I'm
happy to provide additional information/output if someone is interested in
helping me with this problem.
Thank you,
Laura
Linear mixed model fit by REML
Formula: PTR ~ Test * Group + (1 | student)
Data: ptr
AIC BIC logLik deviance REMLdev
-625.7 -559.8 323.9 -706.5 -647.7
Random effects:
Groups Name Variance Std.Dev.
student (Intercept) 0.0010119 0.03181
Residual 0.0457782 0.21396
Number of obs: 2952, groups: studentID, 20
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.547962 0.016476 33.26
Testtest2 -0.007263 0.015889 -0.46
Testtest1 -0.050653 0.016305 -3.11
GroupNoRepNTP 0.008065 0.022675 0.36
GroupRepNTP -0.018314 0.025483 -0.72
Testtest2:GroupNoRepNTP 0.006073 0.021936 0.28
Testtest1:GroupNoRepNTP 0.013901 0.022613 0.61
Testtest2:GroupRepNTP 0.046684 0.024995 1.87
Testtest1:GroupRepNTP 0.039994 0.025181 1.59
Note: The reference level for Test is Test3. The reference level for Group is
RepTP. The interaction p value (after running pvals.fnc with the MCMC) for
Testtest2:GroupRepNTP is p = .062 which I'm willing to accept and interpret
since speech data with English Language Learners is particularly variable.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
