¥Choose between
two types of analysis for each factor: fixed and random
effects
¥Fixed
effects factor = differences between levels in this factor are
modeled as deterministic differences in the mean measurements (as in 3dANOVA and 3dttest)
HUseful
for most categories under the experimenterÕs control or observation
HAllows
same type of statistics as 3dANOVA:
¥factor main effect (are all the mean
activations of each level in this factor the same?)
¥differences between level pairs (e.g.,
level #2 same as #3?)
¥more complex contrasts (e.g.,
average of levels #1 and #2 same as level #3?)
HIf
both factors are modeled as fixed effects with multiple measurements (e.g.,
subjects):
åCan
also test for interaction between the factors
íAre
there any combinations of factor levels whose means Òstick outÓ [e.g.,
mean of cell #(A1,B2) differs from (#A1 mean)+(#B2 mean)]?
íExample:
A=stimulus type, B=drug type; then cell #(A1,B2)
is FMRI response (in each voxel) to stimulus #1 and drug #2
íInteraction
test would determine if any individual combination of drug type and stimulus type was abnormal
¥e.g., if stimulus #1 averages a
high response, and drug #2 averages no effect on response, but when together, value in cell
#(A1,B2)
averages small
¥i.e., Effect of one factor
(stimulus) depends on level of other factor (drug)
¥no interaction means the effects of the
factors are always just additive
åInter-factor
contrasts can then be used to test individual combinations of cells to determine which cell(s) the interaction comes from