Dear Alfonso and Forum members,

I would like to compare different groups. Groups are diagnosis groups in an RCT for different anxiety disorders.
We have 8 different sites and therefore different scanners.

My Model

[1 -1 0 0 0 0 0 0 0 0 0 0]

[patients controls age sex center1 center2 center3 center4 center5 center6 center7 center8]

I am not sure if this works. The results explorer and folder is named

"patients(1).controls(-1).age(0).sex(0).Dr7573199821223284"

I would have expected:

"patients(1).controls(-1).age(0).sex(0).center1(0).center2(0).center3(0).center4(0).center5(0).center6(0).center7(0).center8(0)"

I chose user defined between subject contrast and inserted [1 -1 0 0 0 0 0 0 0 0 0 0], as it is not possible to choose this contrast in the drop down menu.


How can I check if CONN calcualted the correct model?

Thanks
Till

Dear Till,

I wonder whether the model is overparameterized because you should only need 7 variables for dummy-coding the 8 centers/ scanners (i.e. scanner 8 implicitly specified by a "0" for all seven regressors)?

Best wishes,

Marcel


Originally posted by Till Langhammer:

Hey Marcel,

no clue what u mean...every center covariate is dummy coded and tells if the very partticipant is scaned in the scanner or not. so ever participant hat only one center covariate on 1...I want to control for scanner so I include all the scanner covariates and set them to zero so the model uses them as control variables besides the two group variables...

one more question: Do I need a covariate for male and female ? like with the scanner regressors? I think yes...?!!

PLEASE HELP :-/



Originally posted by Marcel Daamen:

Dear Till,

it is probably easier t start with your second question: I think that two regressors would only make sense if you also want to test male main effects or female main effects independently. If you're only interested in testing/controlling differences between men and women, one regressor should be sufficient.... according to regression logic, one sex serves as "active condition", the other as "baseline" (e.g. "female versus not female").

By extension, the same should apply to your scanner issue: To control differences between N scanners, a regression analysis should only need N-1 dummy regressors, because the "missing" scanner is coded implicitly by the baseline (i.e. being coded "0" for each of the N-1 regressors).

I usually refer to the multiple regression chapter in Andy Field's "Discovering Statistics using IBM SPSS" textbook for a more convincing explanation: Meanwhile, this homepage may also be illustrative.

Best wishes,

Marcel


Originally posted by Till Langhammer:

Hi Till and Marcel,

Regarding over-parametrized models, that is perfectly fine. The general linear model handles rank-deficient design matrices without any problem. In this particular case, that also means that you will get exactly the same results to your [-1 1 0 0... 0] contrast evaluating between-group differences whether you enter the 8-sites as eight 0/1 control covariates or as seven mean-centered control covariates (and the same for sex, you may enter that as a single 1/-1 variable, or as two 0/1 variables, or a single 0/1 variable, and the results of the between-group difference analysis will not change at all)

If you want to double-check that the design is correct I would suggest to click on the 'n=..." text on the second-level results tab, which will bring up the design matrix and stat details. You should see something like the image attached (this example that is just for a two-group comparison with three control sites and a [-1 1 0 0 0] contrast). In the bottom-right corner, the "5 predictors (4 independent)" text tells you that this design had 5 regressors, but it is over-parameterized as it has only 4 independent factors (because in this example the three sites were entered using three 0/1 control covariates). In Till's case, with an analysis including [patients controls age sex center1 center2 center3 center4 center5 center6 center7 center8] and a [-1 1 0 0 ... 0] contrast, you should see "12 regressors (11 independent)", and if you decide to enter sex as two different covariates, e.g. [patients controls age male female center1 center2 center3 center4 center5 center6 center7 center8] you would then see "13 regressors (11 independent)", as that additional regressor does not add any information to the design. 

Hope this helps
Alfonso


Originally posted by Marcel Daamen:

Thank you so much, Alfonso! I was very unsure. Now I am good.




Originally posted by Alfonso Nieto-Castanon: