Include Item as a fixed effect?
And consider some "transformation" of your Item variable into several
orthogonal variables that might yield a more useful output than including
one factor with so many levels, 1-1-1, 1-1-2 ... etc.?

Dan

On Tue, Nov 10, 2009 at 11:57 AM, Antoine Tremblay <trea26@gmail.com> wrote:

> Thank you Jonathan for your swift reply. This is very helpful.
>
> Could I get your 2-cents on the following question?
>
> What if in your experiment you have ALL possible members of the population,
> as in ours where we have all possible sequences 1-1-1, 1-1-2, 1-1-3 ...
> 4-4-4? In our case, Items is not a random variable as Subject are because
> we
> did not take a sample from the population, we HAVE the population.
>
> Now, in such a case, do you think it is still warranted to include (1|Item)
>  for example in your model?
>
> On the one hand we should not include Items as a random effect given that
> it
> is not a random variable. On the other hand, a log-likelihood ratio test
> (e.g., model A without (1|Item) and model B with (1|Item)) tells us that
> the
> model with the by-item intercepts is a significant improvement in terms of
> goodness of fit?
>
> What should we do? Simply not include Item as a random effect?
>
> Thanks again
> :-)
> Antoine
>
> On Tue, Nov 10, 2009 at 11:12 AM, Jonathan Baron <baron@psych.upenn.edu
> >wrote:
>
>  > An attempt at an answer to part of ONE of your questions:
> >
> > On 11/10/09 09:58, Antoine Tremblay wrote:
> > > It is our understanding that including participants and items in the
> > random
> > > effect structure (in a mixed-effect regression) not only deals with
> > issues
> > > of generalizability ...
> >
> > I think that if you are after generalizability in Clark's ("Language
> > as fixed-effect fallacy") sense, you need more than this.  I think you
> > need random slopes as well as the random intercepts that you get from
> > simply including "+ (1|Subject) + (1|Item)".  That is, you need to ask
> > whether the variance across subjects or items in the size of the
> > effect is too big.  Thus, for a variable X, you need something like
> >
> > (1+X|Subject) + (1+X|Item)
> >
> > If you use pvals.fnc() in languageR, this won't work, because it won't
> > deal with correlated random effects.  So you have to do something
> > like:
> >
> > (1|Subject) + (0+X|Subject) + (1|Item) + (0+X|Item)
> >
> > which assumes that the random effects are independent.  I think you
> > can make them independent by centering (subtracting the mean from each
> > value) X and Y (the dependent variable) before including them.
> >
> > On the other hand, I have argued that, if one-tailed tests are
> > appropriate, as they usually are in this sort of work, then
> >
> > (1|Subject)+(1|Item)
> >
> > is perfectly fine.  If it is "significant", it tells you that some
> > subject and some item show the effect of interest, even if other
> > subjects and other items go the other way (but less).  Arguably, this
> > is all you can ever know.
> >
> > I'm saying this in part because I'd like to hear what others have to
> > say.
> >
> > Jon
> > --
> > Jonathan Baron, Professor of Psychology, University of Pennsylvania
> > Home page: http://www.sas.upenn.edu/~baron
> >
>
>
>
> --
> Antoine Tremblay
> Department of Neuroscience
> Georgetown University
> Washington DC
>
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>
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