The effects of adding by-subject or by-item random intercepts

Thank you for your replies.
:-)

Let's see if I understood well. Say I test for a frequency effect
(high vs low) ReactionTime ~ Frequency without the by-items random
intercepts and find a difference.

Now in a model where I do include by-items random intercepts
ReactionTime ~ Frequency + (1|Item) the frequency effect disappears.

Then the frequency effect found in the model without by-item random
intercepts was spurious, i.e., was due only to within group
variability and not to a true population effect. Is that right?

Now say I have created artificial items, which I used in my
experiment. I thus have all the whole population of items. Should I
still include Items as a random effect? If it is the case, then,
including or not a random effect is not only a matter of wanting to
generalize over subjects or items, but rather a matter of getting rid
of, so to speak, within-group variability, which, if uncontrolled for,
may lead to spurious effects. Is that right?

Thank you again for your help.
Sincerely,

Antoine

On Tue, Dec 8, 2009 at 7:38 AM, Daniel Ezra Johnson
<danielezrajohnson at gmail.com> wrote:

Shrinkage is not the main issue, as I see it here. When the  
predictor of
interest is Sex you should include by-subject random effect(s),  
when it's
Frequency you should include by-item. Probably you should include  
both in
both cases. You can't do accurate hypothesis testing on Sex and  
Frequency if
you ignore the variation among Subjects and Items.

On Dec 8, 2009, at 2:15 AM, Antoine Tremblay <trea26 at gmail.com>  
wrote:

Dear all,

This question is about the effects of adding by-subject or by-item
random intercepts to a model.

If we are contrasting a single condition between two subject groups,
say ReactionTime ~ Sex,
is it warranted (or necessary or ill-advised) to include by-subjects
random intercepts,
since this could (if I'm understanding it correctly) adjust the mean
reaction time for each subject (and thus for
each condition) towards the grand mean, thus reducing or
eliminating the difference in the condition between subjects? And
similarly if we are contrasting a
single condition between two sets of items, say ReactionTime ~  
Frequency?

I believe that the addition of the random effect may reduce the  
effect
of the fixed effect, but should
not remove it entirely. Is this right?

The question would then become: Why would the addition of say by- 
item
random intercepts to a model
take away an effect that was present in a model without by-item  
random
intercepts?

Thank you again, your help is well appreciated.

--
Antoine Tremblay
Department of Neuroscience
Georgetown University
Washington DC

-- 
Antoine Tremblay
Department of Neuroscience
Georgetown University
Washington DC