Hi Andrew,

As replies to emails have been a bit sporadic lately I'll attempt to answer
your questions. There are more knowledgeable folk out there though...
2 is easiest I think and sort of leads into 3. I get the impression that
the answer to 1 is a bit more personal taste.
2) Depending on what exactly you want to do, yeah, its probably ok. What
youre doing is testing the parameter/estimate of the related and unrelated
levels separately against the nonword level. So youre not comparing the 3
means (i.e. the answer to 3 - it doesnt). You might want to read up a
little on contrasts perhaps as theres various options. Your professor
perhaps wants an F-Test and post-hoc (Tukey or whatever), in which case you
could use the LMERConvenienceFunctions:::pamer.fnc function (this provides
conservative and anticonservative p-values for the effect as a whole,
TarCond; there are other possibilities for this too - see the MixMod
package for instance) or fit a model with and without the IV and do a
likelihood ratio test which would provide you with something similar.


HTH

Alan



--------------------------------------------------
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On 13 September 2012 03:24, Obermeier Andrew <andrewobermeier@me.com> wrote:

> Hello Mixed Modelers,
>
> I came to this list with a question a couple months ago, and will admit it
> was a very humbling experience. There were a few kind responses, but my
> question was mostly ignored or I was told I needed to read basic statistics
> books. I take full blame for this, and know that I was not asking my
> question in the right way. Now, 2 months later, I am trying again.
>
> I am not a statistician but want to learn. Especially, I like learning
> statistics with R because it helps me to see the nuts and bolts of
> statistics much more than the big commercial user interface packages do,
> and I get a much better feel for how to look at data.
>
> I am writing a dissertation in second language acquisition, analyzing my
> data using lme4 for a mixed model with subjects and items set as random
> effects. My dependent variable is reaction time (RT). My independent
> variable is Target Condition (TarCond). TarCond is a three-level factor:
> nonword, related, unrelated.
>
> When I run the model, lmer provides the summary below. The Fixed effects
> section tells me that for the three factors of my independent variable I
> get the following mean reaction times (RT), and all of these have big t
> values:
> nonword (Intercept): 1239.64
> TarCondrelated: 1239.64 - 370.72 = 868.92
> TarCondunrelated: 1239.64 + 318.34 = 1557.98
>
> (lmer summary)
> -----------------------------------------------------------------
> Linear mixed model fit by REML
> Formula: RT ~ TarCond + (1 | Subject) + (1 | Item)
>    Data: postest
>    AIC   BIC logLik deviance REMLdev
>  30949 30982 -15468    30966   30937
> Random effects:
>  Groups   Name        Variance Std.Dev.
>  Item     (Intercept)  22371   149.57
>  Subject  (Intercept)  65149   255.24
>  Residual             560665   748.78
> Number of obs: 1920, groups: Item, 96; Subject, 20
>
> Fixed effects:
>                  Estimate Std. Error t value
> (Intercept)       1239.64      65.61  18.894
> TarCondrelated    -370.72      56.13  -6.605
> TarCondunrelated   318.34      56.13   5.672
>
> Correlation of Fixed Effects:
>             (Intr) TrCndr
> TarCondrltd -0.285
> TarCndnrltd -0.285  0.333
> ----------------------------------------------------------------------
>
>
> Since the t values in the above model were all well above 2 or below -2, I
> read that this might mean I have a significant effect.
>
> I followed the analysis in Baayen, Davidson, and Bates' (2007) article
> titled "Mixed-effects modeling with crossed random effects for subjects and
> items." Though this article informs me that p-values calculated with the
> degrees of freedom used by pvals.fnc will be "anti-conservative", I
> proceeded to analyze the fixed effects in this model with Markov chain
> Monte Carlo sampling.
>
>
> -------------------------------------------------------------------------------------------------
> > mcmcpostest <- pvals.fnc(postest.lmer, nsim = 10000)
> > mcmcpostest$fixed
>                  Estimate MCMCmean HPD95lower HPD95upper  pMCMC Pr(>|t|)
> (Intercept)        1239.6   1239.5     1108.9     1365.0 0.0001        0
> TarCondrelated     -370.7   -371.2     -474.6     -263.8 0.0001        0
> TarCondunrelated    318.3    317.8      214.3      428.8 0.0001        0
>
> -------------------------------------------------------------------------------------------------
>
> My apologies for this long preamble, but my questions are as follows.
> (1) Is this analytical procedure appropriate?
> (2) Specifically, is it OK for me to test the significance of a 3-level
> factor in this way? (My advising professor tells me that the t value can
> only be used to compare 2 means.)
> (3) How does lme4 calculate t values for a factor that is greater than 2
> levels.
>
> Below I have attached my dataset in CSV format. If there is any other
> information I need to provide, please let me know and I will very gladly do
> so.
>
> My apologies if my questions are misguided or inappropriate. I am way out
> of my league here.
>
> I would be very grateful for any attention that anyone can offer to my
> questions.
>
> Sincerely,
>
> Andrew Obermeier
> Associate Professor, Kyoto University of Education
> PhD Candidate, Temple University Japan
>
>
>
>
>
>
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>
>

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