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help > RE: modeling interaction with continuous predictors
Dec 8, 2021 10:12 PM | Andrew Zalesky
RE: modeling interaction with continuous predictors
Hi Hamed,
if you are only interested in the age x behavior interaction, there is no need to demean. The results will be the same with and without demeaning. But remember to include a column of 1's in your design matrix. (Note that this is not the case for the main effects.)
You are right about the F-test. An alternative is to consider two one-sided t-tests, as you have suggested. This will assist with parsing the direction of the effect. It is also possible to conduct a post hoc analysis investigating the direction of the effects.
Andrew
Originally posted by Hamed Zivari:
if you are only interested in the age x behavior interaction, there is no need to demean. The results will be the same with and without demeaning. But remember to include a column of 1's in your design matrix. (Note that this is not the case for the main effects.)
You are right about the F-test. An alternative is to consider two one-sided t-tests, as you have suggested. This will assist with parsing the direction of the effect. It is also possible to conduct a post hoc analysis investigating the direction of the effects.
Andrew
Originally posted by Hamed Zivari:
Dear NBS users
I am trying to model if the association of SC and Behavior is moderated by Age. So the model is SC ~ Age + Behavior + Age x Behavior interaction. Of note that both predictors are continuous.
I was wondering if the current GLM design and contrasts are correct? Is demeaning necessary?
1 age1_de beh1_de age1_de x beh1_de
1 age2_de beh2_de age2_de x beh2_de
. . . .
1 ageN_de behN_de ageN_de x behN_de
Contrast t-test= [0 0 1] and [0 0 -1]
Contrast F-test= [0 0 1]
My next question is how to interpret and report the results since using the F-test one may find a significant subnetwork that includes edges with positive and negative interaction effects. Should I investigate every and each edge (which could be many) in the significant network to see the direction of effects or, eg., use the average connectivity across the significant network to perform this check (this perhaps makes more sense in the case of t-test)?
Thanks in advance for the help.
Best regards
Hamed
I am trying to model if the association of SC and Behavior is moderated by Age. So the model is SC ~ Age + Behavior + Age x Behavior interaction. Of note that both predictors are continuous.
I was wondering if the current GLM design and contrasts are correct? Is demeaning necessary?
1 age1_de beh1_de age1_de x beh1_de
1 age2_de beh2_de age2_de x beh2_de
. . . .
1 ageN_de behN_de ageN_de x behN_de
Contrast t-test= [0 0 1] and [0 0 -1]
Contrast F-test= [0 0 1]
My next question is how to interpret and report the results since using the F-test one may find a significant subnetwork that includes edges with positive and negative interaction effects. Should I investigate every and each edge (which could be many) in the significant network to see the direction of effects or, eg., use the average connectivity across the significant network to perform this check (this perhaps makes more sense in the case of t-test)?
Thanks in advance for the help.
Best regards
Hamed
Threaded View
Title | Author | Date |
---|---|---|
Hamed Zivari | Dec 8, 2021 | |
Selma Lugtmeijer | Mar 10, 2022 | |
Andrew Zalesky | Mar 11, 2022 | |
Selma Lugtmeijer | Mar 11, 2022 | |
Andrew Zalesky | Mar 11, 2022 | |
Marius Gruber | Dec 22, 2021 | |
Andrew Zalesky | Dec 22, 2021 | |
Andrew Zalesky | Dec 8, 2021 | |