[R-sig-ME] The effects of adding by-subject or by-item random intercepts
trea26 at gmail.com
Tue Dec 8 19:19:17 CET 2009
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.
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
>> Thank you again, your help is well appreciated.
>> Antoine Tremblay
>> Department of Neuroscience
>> Georgetown University
>> Washington DC
>> R-sig-mixed-models at r-project.org mailing list
Department of Neuroscience
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