¥Random
effects factor = differences between levels in this factor are
modeled as random fluctuations
HUseful
for categories not under experimenterÕs control or observation
HIn
FMRI, is especially useful for subjects; a good rule is
åtreat subjects as a separate
random effects factor rather than
åas multiple independent
measurements inside fixed-effect factors
HIn
such a case, usually have 1 measurement per cell (each cell is the combination
of a level from the other factor with 1 subject)
åThis
is sometimes called a Òrepeated measures ANOVAÓ, when we have multiple measurements on each subject (in this case, across
different stimulus classes)
HTreating
subjects as a random factor in a fully crossed analysis is a generalization of the paired t-test
åintra-subject
and inter-subject data variations are modeled separately
åwhich
can let you detect small intra-subject changes due to the fixed-effect factors that might otherwise be overwhelmed by larger
inter-subject fluctuations
HMain
effect for a random effects factor tests if fluctuations among levels in this
factor have additional variance above that from the other
random fluctuations in the data
åe.g.,
Are inter-subject fluctuations bigger than intra-subject fluctuations?
åNot
usually very interesting when random factor = subject
HIt is
hard to think of a good FMRI example where both factors would be random
H3dANOVA2: Usually have 1 fixed factor
and 1 random factor = mixed effects analysis