help > Contrast Definition
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Feb 12, 2018 08:02 PM | Humza Ahmed
Contrast Definition
Hello All,
I am having some difficulty interpreting the differences between the following contrasts:
1) AllSub, behav1
[0 1]
2) behav1
[1]
3) AllSub, behav1, behav2
[0 1 0; 0 0 1]
4) Patients, Controls, behav1
[1 -1 0]
How would (1) be different from (2)? Particularly why would AllSub need to be selected to do a regression of behav1?
If (4) is a one-way Ancova controlling for behav1 would (3) essentially be regressions controlling for the other two variables?
From,
Humza Ahmed
I am having some difficulty interpreting the differences between the following contrasts:
1) AllSub, behav1
[0 1]
2) behav1
[1]
3) AllSub, behav1, behav2
[0 1 0; 0 0 1]
4) Patients, Controls, behav1
[1 -1 0]
How would (1) be different from (2)? Particularly why would AllSub need to be selected to do a regression of behav1?
If (4) is a one-way Ancova controlling for behav1 would (3) essentially be regressions controlling for the other two variables?
From,
Humza Ahmed
Feb 14, 2018 11:02 AM | Alfonso Nieto-Castanon - Boston University
RE: Contrast Definition
Hi Humza,
Regarding the difference between (1) and (2), see this#mce_temp_url# post (briefly, the AllSubs term models the constant/intersect term in a regression model)
Contrast (1) evaluates a bivariate regression model (between connectivity values and behav1 scores). A significant effect there means that connectivty values are associated/correlated with behav scores.
Contrast (3) evalutes a multiple regression model (between connectivity values and both behav1 and behav2 scores). A significant effect there means that connectivity values are associated/correlated with one or both (or some linear combination) of the two behav scores. If, instead, you want to evaluate the correlation/association between connectivity values and behav1 scores after controlling for behav2 associations (e.g. associations between connectivity and behav1 that may be mediated by behav2 scores) you would simply use the contrast [0 1 0] instead.
And you are right that contrast (4) is a one-way ANCOVA where you are evaluating the between-group differences in connectivity after controlling for potential differences in behav1 scores between the two groups.
Hope this helps
Alfonso
Originally posted by Humza Ahmed:
Regarding the difference between (1) and (2), see this#mce_temp_url# post (briefly, the AllSubs term models the constant/intersect term in a regression model)
Contrast (1) evaluates a bivariate regression model (between connectivity values and behav1 scores). A significant effect there means that connectivty values are associated/correlated with behav scores.
Contrast (3) evalutes a multiple regression model (between connectivity values and both behav1 and behav2 scores). A significant effect there means that connectivity values are associated/correlated with one or both (or some linear combination) of the two behav scores. If, instead, you want to evaluate the correlation/association between connectivity values and behav1 scores after controlling for behav2 associations (e.g. associations between connectivity and behav1 that may be mediated by behav2 scores) you would simply use the contrast [0 1 0] instead.
And you are right that contrast (4) is a one-way ANCOVA where you are evaluating the between-group differences in connectivity after controlling for potential differences in behav1 scores between the two groups.
Hope this helps
Alfonso
Originally posted by Humza Ahmed:
Hello All,
I am having some difficulty interpreting the differences between the following contrasts:
1) AllSub, behav1
[0 1]
2) behav1
[1]
3) AllSub, behav1, behav2
[0 1 0; 0 0 1]
4) Patients, Controls, behav1
[1 -1 0]
How would (1) be different from (2)? Particularly why would AllSub need to be selected to do a regression of behav1?
If (4) is a one-way Ancova controlling for behav1 would (3) essentially be regressions controlling for the other two variables?
From,
Humza Ahmed
I am having some difficulty interpreting the differences between the following contrasts:
1) AllSub, behav1
[0 1]
2) behav1
[1]
3) AllSub, behav1, behav2
[0 1 0; 0 0 1]
4) Patients, Controls, behav1
[1 -1 0]
How would (1) be different from (2)? Particularly why would AllSub need to be selected to do a regression of behav1?
If (4) is a one-way Ancova controlling for behav1 would (3) essentially be regressions controlling for the other two variables?
From,
Humza Ahmed