Dear all, I recently came across this paper (kindly see the attached pdf), and I am a bit confused about how these authors have measured the effect size for the MA. They have measured Pearson?s correlation coefficient in two different ways (see section 2.3, page 3). (i). Hedges? g has been transformed to a Pearson?s correlation coefficient. (ii). Or, Pearson?s r has been calculated from F, t, or ?2 data. Is this a correct way of measuring the effect size? if yes, how this can be done?? Then, can we really pool the effect sizes calculated from these two methods for the final analysis??? Thank you, Tharaka -------------- next part -------------- An HTML attachment was scrubbed... URL: <https://stat.ethz.ch/pipermail/r-sig-meta-analysis/attachments/20211221/10d150d7/attachment-0001.html> -------------- next part -------------- A non-text attachment was scrubbed... Name: 1-s2.0-S0167880921005260-main.pdf Type: application/pdf Size: 1143948 bytes Desc: not available URL: <https://stat.ethz.ch/pipermail/r-sig-meta-analysis/attachments/20211221/10d150d7/attachment-0001.pdf>
[R-meta] Effect size calculation
2 messages · Tharaka S. Priyadarshana, James Pustejovsky
1 day later
Hi Tharaka, There are formulas in the literature for converting between a standardized mean difference (of which Hedges' g is an estimate) and the Pearson correlation, as well as from other test statistics (F, t, X^2); See Jacobs & Viechtbauer (2017) and Pustejovsky (2014). However, the formulas are only valid under specific distributional assumptions about the variables involved (e.g., that a continuous predictor has been artificially dichotomized, and that interest is in the correlation between the continuous predictor) and the outcome. For the study you attached, it's not at all clear why the authors would use the Pearson correlation coefficient as the effect size measure. If I understand correctly, they are interested in the effects of contrasting treatment conditions (AES vs. control) so the assumptions for converting from SMD to r would not really make sense here. James Jacobs, P., & Viechtbauer, W. (2017). Estimation of the biserial correlation and its sampling variance for use in meta?analysis. Research synthesis methods, 8(2), 161-180. Pustejovsky, J. E. (2014). Converting from d to r to z when the design uses extreme groups, dichotomization, or experimental control. Psychological methods, 19(1), 92. On Tue, Dec 21, 2021 at 5:32 AM Tharaka S. Priyadarshana
<tharakas.priyadarshana at gmail.com> wrote:
Dear all, I recently came across this paper (kindly see the attached pdf), and I am a bit confused about how these authors have measured the effect size for the MA. They have measured Pearson?s correlation coefficient in two different ways (see section 2.3, page 3). (i). Hedges? g has been transformed to a Pearson?s correlation coefficient. (ii). Or, Pearson?s r has been calculated from F, t, or ?2 data. Is this a correct way of measuring the effect size? if yes, how this can be done?? Then, can we really pool the effect sizes calculated from these two methods for the final analysis??? Thank you, Tharaka
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