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
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Antoine Tremblay Department of Neuroscience Georgetown University Washington DC