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help > RE: REX Results GUI: Meaning of Effect Sizes
Nov 23, 2016 11:11 AM | Julian Roessler - Collegium Helveticum, a joint Research Institute of ETH and University of Zurich
RE: REX Results GUI: Meaning of Effect Sizes
Dear Alfonso
I have another question about the meaning of the sign of the effect size in the REX displays:
In the seed to voxel analysis "Treatment : Placebo : Score_Total_Treatment : Score_Total_Placebo [1 -1 0 0]" we found significant positive connectivity to a particular ROI.
Then, our symptomscores can be divided into two subscores. So using the REXtoolbox we did the analysis for each subscore:
(A) Starting with the ROI from our seed2voxel analyis (see above) we opened the REX GUI for this particular ROI and loaded the SPM file of our 2nd level analysis for "Treatment : Placebo : subscore1_Treatment : subscore1_Placebo [0 0 1 -1]", here we got a significant positive effect.
(B) Same as above for: "Treatment : Placebo : subscore2_Treatment : subscore2_Placebo [0 0 1 -1]" here we got a significant negative effect.
How can we interpret the results?
Does the positive effect in (A) mean that the connectivity is even more positive for the subscore1 in interaction with the treatment, and the negative value in (B) that this effect is a less positive in interaction with subscore2,
OR that the connectivity is now negative in interaction with subscore 2 (and thus reflects an anticorrelation for those who score high on subscore2 under the influence of the treatment)?
Kind regards
Julian
I have another question about the meaning of the sign of the effect size in the REX displays:
In the seed to voxel analysis "Treatment : Placebo : Score_Total_Treatment : Score_Total_Placebo [1 -1 0 0]" we found significant positive connectivity to a particular ROI.
Then, our symptomscores can be divided into two subscores. So using the REXtoolbox we did the analysis for each subscore:
(A) Starting with the ROI from our seed2voxel analyis (see above) we opened the REX GUI for this particular ROI and loaded the SPM file of our 2nd level analysis for "Treatment : Placebo : subscore1_Treatment : subscore1_Placebo [0 0 1 -1]", here we got a significant positive effect.
(B) Same as above for: "Treatment : Placebo : subscore2_Treatment : subscore2_Placebo [0 0 1 -1]" here we got a significant negative effect.
How can we interpret the results?
Does the positive effect in (A) mean that the connectivity is even more positive for the subscore1 in interaction with the treatment, and the negative value in (B) that this effect is a less positive in interaction with subscore2,
OR that the connectivity is now negative in interaction with subscore 2 (and thus reflects an anticorrelation for those who score high on subscore2 under the influence of the treatment)?
Kind regards
Julian
Threaded View
| Title | Author | Date |
|---|---|---|
| Julian Roessler | Oct 19, 2016 | |
| Alfonso Nieto-Castanon | Oct 19, 2016 | |
| Yana Panikratova | Oct 1, 2019 | |
| Alfonso Nieto-Castanon | Nov 10, 2022 | |
| Yana Panikratova | Oct 21, 2023 | |
| Julian Roessler | Dec 18, 2017 | |
| Athena Demertzi | Nov 25, 2016 | |
| Julian Roessler | Nov 23, 2016 | |
| Alfonso Nieto-Castanon | Nov 23, 2016 | |
| Julian Roessler | Nov 25, 2016 | |
