In general, I would recommend treating sex as a categorical rather than
continuous variable. To do this, you would have 6 groups (your 3 x 2
sex). I'm not sure what you are trying to test for. Eg, are you
interested in the difference between groups regressing out the effects
of age, DD, and sex? Or are you interested in, eg, the effect of age
regressing out the other stuff?
On 6/14/2023 3:59 PM, Cristina A. F. Román wrote:
External Email - Use Caution
Hello,
I am looking to run a MANCOVA to examine cortical gray matter
differences across three groups (accounting for several covariates),
and I was hoping to get input on whether I am setting up my design
matrix correctly.
Groups: 3
Covariates: Age (demeaned), disease duration (DD; demeaned), sex
Simplified Example:
Group EV1(Grp1) EV2(Grp2) EV3(Grp3) EV4(age) EV5(DD)
EV6(sex)
1 1 0 0 0.25
0.75 -1
1 1 0 0 0.25
0.25 -1
1 0 1 0 -0.75
-0.25 1
1 0 1 0 0.5
-0.75 1
1 0 0 1 -0.5
0.5 1
1 0 0 1 0.25
0.25 -1
Questions:
1. Is this an appropriate way to do a three-group comparison with
covariates? Or, is it better to just run three separate t-tests
(including covariates)?
2. Regarding post-hoc testing, would I just do three separate t-tests?
This issue here, I imagine, is that the individual t-test results may
not reflect the original MANCOVA results (e.g., post-hoc testing
reveals a new significant area that was not significant in the MANCOVA).
3. One other option is to run the initial MANCOVA within a statistical
program (i.e., using global cortical gray matter volumes) and then
follow up with postdoc testing within FSL (i.e., t-tests). Would this
make sense?
Thanks in advance for your time and help,
Cristina
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