Hi Max,
Demeaning can be helpful when studying an interaction effect. If you are looking at the interaction of two main effects, A and B, demeaning A and B ensures that coefficient for A reflects the mean value for this variable in the interaction effect (instead of when the variable is 0, which might not be meaningful). In this way, demeaning can help with interpretting the model, but it won't change statistical signficiance.
The design matrix looks correct. You may also want to study the interaction between group and behaviour rating. This is acheived by multiplying the group and behaviour column. In this case, the group column would need to be removed in the final model.
Best,
Andrew
Originally posted by Max Kathofer :
Hi Andrew!
Thanks for all your responses! I just have one last question. What if I want to model a continuous predictor and check whether there exists a linearly increasing subgraph?
Again I have a repeated measure design across 2 drug conditions but I have ~20 FC matrices for each drug condition per subject. I would have 2 questions:
- It is often recommended to demean data across ALL participants. I am unsure why though. Wouldn't it make more sense to demean for each subject across drug conditions to account for their individual differences?
- Would a design matrix like this then be correct?
0 -2 1 0 0 0 ....
0 3 1 0 0 0 ....
1 -0.5 1 0 0 0 ....
0 -8 0 1 0 0 0
The first column denotes drug (0/ placebo; 1/drug)
The second column is the per subject demeaned behavioral rating
Column 3 denotes that the first 3 entries belong to participant 1
Column 4 denotes that the 4th entry belongs to subject 2
The zeros afterwards would have length(subejct)
Thank you for your help!
Cheers,
Max
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Title | Author | Date |
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David de Wide | Aug 3, 2017 | |
Max Kathofer | Oct 31, 2024 | |
Andrew Zalesky | Nov 1, 2024 | |
Max Kathofer | Nov 1, 2024 | |
Andrew Zalesky | Nov 1, 2024 | |
Max Kathofer | Dec 2, 2024 | |
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