Dear all,
Reading SPM 99 documentation, I have understood that the statistics
corresponding
to "random effect" are done using a two-stage approach, i.e. calculating
one contrast image for each subject (as if only one determination has been
performed on each subject, so that the residual df is number_of_subject-1)
and then running a second level analysis (I did not find out how this
analysis is performed. Is it by comparing the mean t value to 0?). Is this
correct?
In books concerning variance analysis, the random effect (mixed models) is
generally performed by calculating a F value as the ratio : (main effect
linked variance) / (interaction variance). Then, the interaction df is:
(number_of_subject-1)*(number_of_replication_per_subject - 1). The
contrasts of interest are then calculated in the same way than in SPM but
the interaction variance is taken as residual variance.
My questions are:
1) Did I correctly understood the random analysis in SPM?
2) As the number of values for the contrast is always low (the number
of subjects), is it better to use a non parametric test to compare the mean
t value to 0?
3) Is the first order risk (false positive) in the two-stage approach in
SPM the same (or lower or greater) as in the classical approach (one_stage
analysis, F determination and contrasts deducting using of interaction
variance in place of between replicates variance)?
4) The same question for second order risk (power).
Yours,
Pierre Fonlupt
Unité INSERM 280
151 crs A. Thomas
69008 LYON
email: [log in to unmask]
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|