[R-meta] [External] RE: Effect Sizes and Beta Coefficients
Hall, Rebecca
r@h@||5 @end|ng |rom |@nc@@ter@@c@uk
Wed Jun 12 17:55:51 CEST 2024
Dear Wolfgang,
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)?
Further to this, please could you advise how I might then calculate the partial correlation from a one-sample t-test?
Best,
Rebecca
Dr Rebecca Hall | Research Associate
Department of Psychology | Lancaster University
[cid:e278ead7-3a22-4727-8d85-17c7a87a554a] [cid:349cef11-e009-4261-9b73-e71cd423e99d]
________________________________
From: Viechtbauer, Wolfgang (NP) <wolfgang.viechtbauer using maastrichtuniversity.nl>
Sent: 11 June 2024 17:21
To: R Special Interest Group for Meta-Analysis <r-sig-meta-analysis using r-project.org>
Cc: Hall, Rebecca <r.hall5 using lancaster.ac.uk>
Subject: [External] RE: [R-meta] Effect Sizes and Beta Coefficients
This email originated outside the University. Check before clicking links or attachments.
Dear Rebecca,
This is my personal opinion: I would consider this approach outdated.
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.
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:
Aloe, A. M. (2015). Inaccuracy of regression results in replacing bivariate correlations. Research Synthesis Methods, 6(1), 21-27. 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<https://doi.org/10.1002/jrsm.1126>
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).
Best,
Wolfgang
> -----Original Message-----
> From: R-sig-meta-analysis <r-sig-meta-analysis-bounces using r-project.org> On Behalf
> Of Hall, Rebecca via R-sig-meta-analysis
> Sent: Tuesday, June 11, 2024 17:57
> To: r-sig-meta-analysis using r-project.org
> Cc: Hall, Rebecca <r.hall5 using lancaster.ac.uk>
> Subject: [R-meta] Effect Sizes and Beta Coefficients
>
> Dear all,
>
> I have a question regarding the use of a beta coefficient as a substitute for
> effect size where a paper lacks statistical data for Pearson's r to otherwise be
> calculated.
>
> Peterson & Brown (2005) support the use of standardised beta coefficients and
> relative SE in the place of correlations, but Roth et al. (2018) criticise this.
> I'm therefore wondering whether there is a general consensus regarding the use
> of beta coefficients, and should Peterson & Brown's approach no longer be
> appropriate then I would be gladly advised on the alternative method that should
> be utilised.
>
> Many thanks,
> Rebecca
>
> Dr Rebecca Hall | Research Associate
> Department of Psychology | Lancaster University
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