Hi:

On Tue, Oct 12, 2010 at 8:59 PM, Laura Halderman <lkh11@pitt.edu> wrote:

> 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.
>

Do you have three groups of subjects, where each subject is tested on three
separate occasions? Are the tests meant to be replicates, or is there some
other purpose for why they should be represented in the model? Based on this
description, it would appear to me that the groups constitute one factor,
the students nested within groups another, with three measurements taken on
each student. How many students per group?

>
> 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.
>

Let's get the design straight first and the model will work itself out...

Dennis

>
> 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.
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>

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