Dear Alfonso
Dear Conn-Users

Previous posts suggest the following 2nd level contrast [0 0 1 -1] for testing the gender by score interaction, using "males" , "females", "score_for_males", "score_for_females", respectively. However, how do you test the same interaction, by simultaneously correcting for covariates of no interest, such as age or sites? Doesn't it confuse the Conn with categorical or continuous variables when you put "0" for these e.g. [0 0 0 0 0 1 -1]? I also assume here the scores are coded with either a value or a "0". Does it additionally require spliting each covariate into "age_males", "age_females", "site_1_male", "site_1_female", "site_2_male", "site_2_female"?

I screened the posts for this specific question, but could find no answer and would appreciate any comment. Thank you.

Greetings,
Lucas

Dear Lucas,

You may simply add covariates of no interest to your design (e.g. "males, females, score*males, score*females, age, site1, site2") and enter 0 at the corresponding place in your contrast (e.g. [0 0 -1 1 0 0 0]). If, in addition, you want to also correct for potential covariate-by-group interactions, that is also perfectly fine, and you may do so by entering those interaction terms in the design as additional covariates of no interest (e.g. "males, females, score*males, score*females, age*males, age*females, site1*males, site1*females, site2*males, site2*females" and a contrast [0 0 -1 1 0 0 0 0 0 0]). Of course, you may also do that differentially across covariates (e.g. include the group*age interactions but not the group*site interactions)

There is no potential confusion regarding continuous vs. categorical covariates here. In both cases you are estimating the expected differential effect in connectivity associated with a unit-increase in scores when everything else (including the covariates) remain constant (i.e. at any arbitrary value of those covariates, whether continuous or categorical), and you are then simply comparing those differential effects between your two groups (males vs. females). 

Hope this helps
Alfonso
Originally posted by Lucas Moro:

Dear Alfonso,

I'm very grateful for your comment.
May I still ask for clarity how to test for a "group by sex" interaction (only between-subject contrasts)?

When having the following strings:
- "cases": coded as 1 and controls as 0
-"controls": coded as 1 and cases as 0
-"males": coded as 1 and females as 0
-"females": coded as 1 and males as 0

will the contrast [1 -1 -1 1] test for such interaction, and [0.5 -05 0.5 -0.5] for the main effect of sex?

Thank you.
Lucas

Dear Lucas,

Almost but not quite; you may test that using a model of the form:

cases*males: coded as 1 for male cases, 0 otherwise
cases*females : coded as 1 for female cases, 0 otherwise
controls*males: coded as 1 for male controls, 0 otherwise
controls*females: coded as 1 for female controls, 0 otherwise

with a contrast [-1 1 1 -1] for a sex by cases interaction, and a contrast [.5 -.5 .5 -.5] for the main effect of sex

Hope this helps
Alfonso
Originally posted by Lucas Moro: