Hi,
I have data sets with 4 sessions per subject, where the 4 sessions are
acquired in direct succession and are virtually identical in terms of
experimental design ( same number of trials/ condition, ISI's etc.).
However, physically they are 4 different time-series data sets.
As far as I understand I could
(i) concatenate these 4 data sets into 1 before doing any preprocessing and
1st level analysis, and then proceed with 1 data set / subject.
(ii) do a Multi-Session & Multi-Subject (Repeated Measures - Three Level )
analysis whereby any preprocessing and 1stlevel analysis is done on the
individual 4 session data sets first. The results are then fed into one 2nd
level analysis. The group mean effect for any given condition would then be
extracted through a 3rd level analysis.
I would appreciate if you could tell me whether both approaches are sound
and why you would recommend one over the other.
Thanks a lot.
best wishes,
Thomas
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