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Dear Wolfgang, </div>
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Many thanks for your response. Am I correct in thinking that I can convert a p-value in R using qt(1- p-value, n-1)? </div>
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Further to this, please could you advise how I might then calculate the partial correlation from a one-sample t-test?</div>
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Best,</div>
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Rebecca</div>
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<b>Dr Rebecca Hall | Research Associate<br>
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Department of Psychology | Lancaster University</div>
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<div id="divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif" style="font-size:11pt" color="#000000"><b>From:</b> Viechtbauer, Wolfgang (NP) <wolfgang.viechtbauer@maastrichtuniversity.nl><br>
<b>Sent:</b> 11 June 2024 17:21<br>
<b>To:</b> R Special Interest Group for Meta-Analysis <r-sig-meta-analysis@r-project.org><br>
<b>Cc:</b> Hall, Rebecca <r.hall5@lancaster.ac.uk><br>
<b>Subject:</b> [External] RE: [R-meta] Effect Sizes and Beta Coefficients</font>
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<div class="PlainText">This email originated outside the University. Check before clicking links or attachments.<br>
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Dear Rebecca,<br>
<br>
This is my personal opinion: I would consider this approach outdated.<br>
<br>
Typically, in a situation like this, one also knows the t-statistic for the coefficient of interest (or its p-value from which one can back-calculate the t-statistic). In that case, one can compute the (semi)partial correlation coefficient for the coefficient.<br>
<br>
However, whether one should combine such 'partial' effect sizes with bivariate correlations is debatable in the first place. A relevant article that essentially argues against this is:<br>
<br>
Aloe, A. M. (2015). Inaccuracy of regression results in replacing bivariate correlations. Research Synthesis Methods, 6(1), 21-27.
<a href="https://doi.org/10.1002/jrsm.1126">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1002%2Fjrsm.1126&data=05%7C02%7Challr4%40live.lancs.ac.uk%7C13428b25f15c401569f808dc8a32a626%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C638537197297828712%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=wEj7B9IYjGpNKGcC%2Ffvo2etZSAzfaqjHAnZa%2FMr%2Fy9Y%3D&reserved=0</a><br>
<br>
At least, one could try to capture some of the heterogeneity introduced by this by including a moderator in the model that indicates the type of correlation coefficient. With enough studies, one could even go a step further and include moderators that indicate
which covariates were included in the original regression models from which the (semi)partial correlations were obtained (as a bunch of dummy variables).<br>
<br>
Best,<br>
Wolfgang<br>
<br>
> -----Original Message-----<br>
> From: R-sig-meta-analysis <r-sig-meta-analysis-bounces@r-project.org> On Behalf<br>
> Of Hall, Rebecca via R-sig-meta-analysis<br>
> Sent: Tuesday, June 11, 2024 17:57<br>
> To: r-sig-meta-analysis@r-project.org<br>
> Cc: Hall, Rebecca <r.hall5@lancaster.ac.uk><br>
> Subject: [R-meta] Effect Sizes and Beta Coefficients<br>
><br>
> Dear all,<br>
><br>
> I have a question regarding the use of a beta coefficient as a substitute for<br>
> effect size where a paper lacks statistical data for Pearson's r to otherwise be<br>
> calculated.<br>
><br>
> Peterson & Brown (2005) support the use of standardised beta coefficients and<br>
> relative SE in the place of correlations, but Roth et al. (2018) criticise this.<br>
> I'm therefore wondering whether there is a general consensus regarding the use<br>
> of beta coefficients, and should Peterson & Brown's approach no longer be<br>
> appropriate then I would be gladly advised on the alternative method that should<br>
> be utilised.<br>
><br>
> Many thanks,<br>
> Rebecca<br>
><br>
> Dr Rebecca Hall | Research Associate<br>
> Department of Psychology | Lancaster University<br>
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