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help > RE: Cohen's d in comparison of 2 independent samples
Nov 10, 2022 09:11 PM | Alfonso Nieto-Castanon - Boston University
RE: Cohen's d in comparison of 2 independent samples
Dear Yana,
You are totally right, thanks for pointing that out, for a two sample t-test the Cohen's d effect-size can be computed using the general formula in https://www.nitrc.org/forum/message.php?msg_id=33850, which, for equal-size groups, will be approximately twice the value I was describing in the example in my previous message (the |T|/sqrt(dof) value in my previous message corresponds to the Cohen's f effect-size, which are the ones that one would generally use in the context of an arbitrary GLM analysis; and when used to describe a two-sample t-test as implemented in GLM Cohen_d ~= 2 Cohen_f)
Hope this helps, and thanks again
Alfonso
Originally posted by Yana Panikratova:
You are totally right, thanks for pointing that out, for a two sample t-test the Cohen's d effect-size can be computed using the general formula in https://www.nitrc.org/forum/message.php?msg_id=33850, which, for equal-size groups, will be approximately twice the value I was describing in the example in my previous message (the |T|/sqrt(dof) value in my previous message corresponds to the Cohen's f effect-size, which are the ones that one would generally use in the context of an arbitrary GLM analysis; and when used to describe a two-sample t-test as implemented in GLM Cohen_d ~= 2 Cohen_f)
Hope this helps, and thanks again
Alfonso
Originally posted by Yana Panikratova:
Dear Alfonso,
Could you please clarify for a non-specialist in statistics,
As far as I have understood, you are writing about Cohen's d in comparison of 2 independent samples (patients vs. controls). Why is not Cohen's d computed in the following way: d = 2T / sqrt(dof)? Everywhere in the literature T is multiplied by 2 in independent samples T-test, e.g.:
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4:863. doi:10.3389/fpsyg.2013.00863
Lots of excuses if the question is strange. Thank you very much for your tool, it's very helpful.
Yours sincerely,
Yana
Originally posted by Alfonso Nieto-Castanon:
Could you please clarify for a non-specialist in statistics,
As far as I have understood, you are writing about Cohen's d in comparison of 2 independent samples (patients vs. controls). Why is not Cohen's d computed in the following way: d = 2T / sqrt(dof)? Everywhere in the literature T is multiplied by 2 in independent samples T-test, e.g.:
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4:863. doi:10.3389/fpsyg.2013.00863
Lots of excuses if the question is strange. Thank you very much for your tool, it's very helpful.
Yours sincerely,
Yana
Originally posted by Alfonso Nieto-Castanon:
Dear Julian,
Generally effect sizes in REX displays correspond to contrast values, or linear combinations of regressor coefficients, from your secod-level general linear model. For your analyses, looking at patient-control differences in connectivity, those effect sizes (approximately 0.05 in your results) will be interpretable as average differences in Fisher-transformed correlation values between the patients and control groups. If you prefer to report Cohen's d, in your case that can be easily computed from your analysis T-stats and dofs as d = T / sqrt(dof) (e.g. T=4.11 and dof probably 54 or 52, not totally sure, so Cohen's d in this case is going to be around 0.5 or a "medium-size" effect)
Hope this helps
Alfonso
Originally posted by Julian Roessler:
Generally effect sizes in REX displays correspond to contrast values, or linear combinations of regressor coefficients, from your secod-level general linear model. For your analyses, looking at patient-control differences in connectivity, those effect sizes (approximately 0.05 in your results) will be interpretable as average differences in Fisher-transformed correlation values between the patients and control groups. If you prefer to report Cohen's d, in your case that can be easily computed from your analysis T-stats and dofs as d = T / sqrt(dof) (e.g. T=4.11 and dof probably 54 or 52, not totally sure, so Cohen's d in this case is going to be around 0.5 or a "medium-size" effect)
Hope this helps
Alfonso
Originally posted by Julian Roessler:
Dear Alfonso
I have a question about the meaning of the effect size (the y-axis in the REX Results GUI). Is this effect size in the sense of cohen's d? Or how should the effect size value be interpreted? Because we get nice significant results, but with a very small effect size - as you can see on the picture I added below.
The analysis we did, was thanks to your help and is described here in detail: https://www.nitrc.org/forum/forum.php?th...
Kind regards
Julian
I have a question about the meaning of the effect size (the y-axis in the REX Results GUI). Is this effect size in the sense of cohen's d? Or how should the effect size value be interpreted? Because we get nice significant results, but with a very small effect size - as you can see on the picture I added below.
The analysis we did, was thanks to your help and is described here in detail: https://www.nitrc.org/forum/forum.php?th...
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 | |