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help > RE: 2x3 Mixed Design ANOVA
Oct 15, 2018 12:10 PM | Andrew Zalesky
RE: 2x3 Mixed Design ANOVA
Hi Ana,
this is not quite what I had in mind. I don't think that your proposed design matrix will work.
I was thinking of the following design matrix:
1 1
1 2
1 4
1 1
1 2
1 4
1 1
1 2
1 4
1 1
1 2
1 4
I have not included the within subjects effects. Need to include another 4 columns to model these effects, as you have done below.
Contrast is [0 1 0 0 0 0] to test for t1 < t2 < t3 and [0 -1 0 0 0 0] to test for t1> t2> t3.
Select "t-test"
The assumption here is that the increase in connectivity between t2 and t3 is exactly twice that of the increase between t1 and t2. Need to assess whether this assumption is realistic.
Note that there are more sophisticated tests to assess parametric increases with time, although they cannot be coded in terms of a GLM.
Andrew
Originally posted by Ana Coelho:
this is not quite what I had in mind. I don't think that your proposed design matrix will work.
I was thinking of the following design matrix:
1 1
1 2
1 4
1 1
1 2
1 4
1 1
1 2
1 4
1 1
1 2
1 4
I have not included the within subjects effects. Need to include another 4 columns to model these effects, as you have done below.
Contrast is [0 1 0 0 0 0] to test for t1 < t2 < t3 and [0 -1 0 0 0 0] to test for t1> t2> t3.
Select "t-test"
The assumption here is that the increase in connectivity between t2 and t3 is exactly twice that of the increase between t1 and t2. Need to assess whether this assumption is realistic.
Note that there are more sophisticated tests to assess parametric increases with time, although they cannot be coded in terms of a GLM.
Andrew
Originally posted by Ana Coelho:
Once again, thank you for your answer Dr.
Zalesky!
Just to clarify some things if I want to evaluate the linear increase: time1 < time2 < time3 and also taking into account that we have 2 groups (good performers vs. poor performers) then my design matrix for an example with 4 subjects will be something like this:
1 1 0 0 0 0 0 1 0 0 0
1 0 2 0 0 0 0 1 0 0 0
1 0 0 4 0 0 0 1 0 0 0
1 1 0 0 0 0 0 0 1 0 0
1 0 2 0 0 0 0 0 1 0 0
1 0 0 4 0 0 0 0 1 0 0
1 0 0 0 1 0 0 0 0 1 0
1 0 0 0 0 2 0 0 0 1 0
1 0 0 0 0 0 4 0 0 1 0
1 0 0 0 1 0 0 0 0 0 1
1 0 0 0 0 2 0 0 0 0 1
1 0 0 0 0 0 4 0 0 0 1
where columns 2 to 4 model time for group1 and columns 5 to 7 model time for group2. The last 4 columns model each subject.
The contrast for linear increase in the group 1 will be: [0 -1 0 1 0 ...] Is this contrast correct? Because you mentioned that contrasts will be [0 1 0 0...] for t1 > t2 > t3 and [0 -1 0 0...] for t1 < t2 < t3, but I don't get it how this reflects the linear increase.
Thanks in advance!
Best regards,
Ana
Just to clarify some things if I want to evaluate the linear increase: time1 < time2 < time3 and also taking into account that we have 2 groups (good performers vs. poor performers) then my design matrix for an example with 4 subjects will be something like this:
1 1 0 0 0 0 0 1 0 0 0
1 0 2 0 0 0 0 1 0 0 0
1 0 0 4 0 0 0 1 0 0 0
1 1 0 0 0 0 0 0 1 0 0
1 0 2 0 0 0 0 0 1 0 0
1 0 0 4 0 0 0 0 1 0 0
1 0 0 0 1 0 0 0 0 1 0
1 0 0 0 0 2 0 0 0 1 0
1 0 0 0 0 0 4 0 0 1 0
1 0 0 0 1 0 0 0 0 0 1
1 0 0 0 0 2 0 0 0 0 1
1 0 0 0 0 0 4 0 0 0 1
where columns 2 to 4 model time for group1 and columns 5 to 7 model time for group2. The last 4 columns model each subject.
The contrast for linear increase in the group 1 will be: [0 -1 0 1 0 ...] Is this contrast correct? Because you mentioned that contrasts will be [0 1 0 0...] for t1 > t2 > t3 and [0 -1 0 0...] for t1 < t2 < t3, but I don't get it how this reflects the linear increase.
Thanks in advance!
Best regards,
Ana
Threaded View
| Title | Author | Date |
|---|---|---|
| Ana Coelho | Oct 10, 2018 | |
| Andrew Zalesky | Oct 10, 2018 | |
| Ana Coelho | Oct 11, 2018 | |
| Andrew Zalesky | Oct 12, 2018 | |
| Ana Coelho | Oct 12, 2018 | |
| Andrew Zalesky | Oct 15, 2018 | |
| Ana Coelho | Oct 15, 2018 | |
| Andrew Zalesky | Oct 16, 2018 | |
| Ana Coelho | Oct 18, 2018 | |
| Andrew Zalesky | Oct 19, 2018 | |
| Ana Coelho | Mar 12, 2019 | |
| Andrew Zalesky | Mar 13, 2019 | |
| Ana Coelho | Mar 13, 2019 | |
| Ana Coelho | Oct 19, 2018 | |
| Ana Coelho | Oct 22, 2018 | |
| Andrew Zalesky | Oct 22, 2018 | |
| Ana Coelho | Oct 25, 2018 | |
| Andrew Zalesky | Oct 26, 2018 | |
| Ana Coelho | Oct 26, 2018 | |
| Andrew Zalesky | Oct 27, 2018 | |