Print

---

From what I have seen in the mailing list, I may not be the first to ask this question, but I am a bit confused by the answers I saw, since I am not sure how they relate to my specific case.

I have a task design that involves emotional pictures (positive, neutral and negative) each followed by one of two possible questions (1 and 2). At the first level, I computed six contrasts per subject, with each question type separately for each emotional valence, versus baseline.

I now would like to assess the role of the valence and question type factors as well as a group factor (patient versus control) at the second level.

From what I understood, the easiest option would be a full factorial model with 3 factors: group, question type and valence. My questions are:

1) Can I use a full-factorial model or the use of multiple contrast from each of the subjects is equivalent to a repeated measures design, which would mean I have to use a flexible factorial design and model a “Subject” factor explicitly? 

2) If I use a full factorial, from what I gather the independence of the factors should be: group(Yes), question(No), valence(No). What about the variance? Should it be unequal, equal, equal or just always unequal? 

3) Another, perhaps easier, possibility would be to just use two groups t-tests and assess each of the 6 conditions. So I would have to run 6 ttests. However if I wanted to, for example, assess the difference across groups in question 1 regardless of valence, can I simply enter all the contrasts of question one (that is for positive, neutral and negative valence) in the “Group 1 scans” box? I assume this would compute the average of all the scans and compare it across groups, which is what I would be looking for, but is there any problem in entering 3 scans per subject in the ttest design? Also, suppose I enter age as a covariate. I then would need to enter the same age 3 times per subject. Would that be a problem?

Sorry if this question has maybe already been answered, I am just still a bit confused.

Thank you very much for your help.

Yours faithfully,