Hi all, we have some questions here about how to model a simulated data
set. We basically have a time series of fixed SOA with only signal
versus noise scans. The first 4 scans are baseline noise + signal (X +
(3*SD)) and the following 8 scans are just baseline noise with no
signal. We tried analysing this dataset with SPM and depending on how we
model it we obtain very different t values. Analysis I= we model only
the stimulus (as an event) and ignore the baseline (i.e. we only enter
one condition (contrast = 1 )); here we found that the t statistic
ranged between 11 and 5 for the simulated active regions. Analysis II =
we model both the stimulus (as an event) and the baseline (as a box car
function); here the t statistic for the same active areas ranged between
3.21 and 1.6. The degrees of freedom were practically the same for both
types of analyses (92.4 and 91.7, respectively). Since our baseline is
not really a baseline (cause we generated no activity in it), we thought
the appropriate way of analysing the dataset was by using analysis I,
but we are not sure anymore. Given the nature of our dataset what should
be the most appropriate way to analyse it, using analysis I or analysis
II?
thanks a lot
valeria
|