[R-meta] [External] RE: Effect Sizes and Beta Coefficients

[Viechtbauer, Wolfgang (NP)]

B/SE gives you the test statistic. If you want to use escalc(measure="PCOR", ...), then you need to supply this via the 'ti' argument, the number of predictors via the 'mi' argument, and the sample size via the 'ni' argument. See:

https://wviechtb.github.io/metafor/reference/escalc.html#partial-and-semi-partial-correlations

> -----Original Message-----
> From: Hall, Rebecca <r.hall5 using lancaster.ac.uk>
> Sent: Tuesday, June 18, 2024 16:26
> To: Viechtbauer, Wolfgang (NP) <wolfgang.viechtbauer using maastrichtuniversity.nl>;
> Michael Dewey <lists using dewey.myzen.co.uk>; R Special Interest Group for Meta-
> Analysis <r-sig-meta-analysis using r-project.org>
> Subject: Re: [R-meta] [External] RE: Effect Sizes and Beta Coefficients
>
> Many thanks to you both for your input.
>
> Wolfgang, please could you expand on how we compute B/SE to get the test
> statistic, and then how we add the number of predictors and the total sample
> size to compute the partial correlation coefficient? Is this through cholesky
> factorisation?
>
> Best,
> Rebecca
>
> Dr Rebecca Hall | Research Associate
> Department of Psychology | Lancaster University
>
> ________________________________________
> From: Viechtbauer, Wolfgang (NP) <wolfgang.viechtbauer using maastrichtuniversity.nl>
> Sent: 18 June 2024 08:46
> To: Michael Dewey <lists using dewey.myzen.co.uk>; 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: RE: [R-meta] [External] RE: Effect Sizes and Beta Coefficients
>
> Adding a bit to this:
>
> With respect to the MANCOVA: What kind of 'effect size' are you trying to
> compute for this? This thread revolved around ((semi)partial) correlation
> coefficients, so I assume this is what you are trying to compute here. I am not
> sure what information you have from the MANCOVA, but generally I would say that
> it will be next to impossible to compute a correlation coefficient from such an
> analysis. Test statistics from a MANCOVA will not tell you about directionality
> and are a complex amalgamation of the group differences for multiple variables
> (adjusted for covariates). Things already get tricky if we take away the
> 'multivariate' part and take away the 'covariate' part, in which case we are
> left with an independent samples t-test. Translating the results from such a
> test into a type of correlation coefficient that can meaningfully be combined
> with the results from studies that measured both variables of interest on a
> continuous scale (and then report of regular Pearson product-moment correlation
> coefficient) requires assuming that the dichotomous variable that defines the
> two groups for the t-test is a dichotomized version of a continuous variable in
> which case one can compute a biserial correlation coefficient. Trying to do
> something like this based on an MANCOVA seems dubious at best.
>
> As for your second question: As far as I can tell, the table you show does not
> appear to provide odds ratios, but the results from linear regression models
> (leaving aside that the table appears to be messed up, at least with respect to
> the last three columns). The values shown are beta coefficients (since outcome
> and predictors were z-scored -- which makes the B and beta columns redundant).
> If you want a partial correlation coefficient here, you can just compute B/SE to
> get the test statistic. Together with the number of predictors and the total
> sample size, one can then compute the partial correlation coefficient from this
> information. But I cannot tell you which of the models you should use for this
> (this is a substantive question).
>
> Best,
> Wolfgang
>
> > -----Original Message-----
> > From: Michael Dewey <lists using dewey.myzen.co.uk>
> > Sent: Monday, June 17, 2024 17:13
> > To: R Special Interest Group for Meta-Analysis <r-sig-meta-analysis using r-
> > project.org>; Viechtbauer, Wolfgang (NP)
> > <wolfgang.viechtbauer using maastrichtuniversity.nl>
> > Cc: Hall, Rebecca <r.hall5 using lancaster.ac.uk>
> > Subject: Re: [R-meta] [External] RE: Effect Sizes and Beta Coefficients
> >
> > Dear Rebecca
> >
> > In general estimates adjusted for different sets of covariates are not
> > directly comparable. If the sets are almost the same then you may be
> > able to argue for including them. It might also depend on strategic
> > questions like how many studies you already have and whether adding one
> > more is worth the hassle of explaining why you included it.
> >
> > In short as so often the answer is, how brave do you feel?
> >
> > Michael
> >
> > On 17/06/2024 15:27, Hall, Rebecca via R-sig-meta-analysis wrote:
> > > Dear Wolfgang,
> > >
> > > Many thanks for your assistance so far. We are still looking at papers
> > > using various statistical analyses, and have come across one which
> > > utilises a one-way MANCOVA. Please could you advise on the validity of
> > > calculating an effect size from this type of analysis, as the reported F
> > > and p values are altered by controlling for covariates, whereas this has
> > > not been the case in other studies.
> > >
> > > Similarly, we have a paper reporting odds ratios which, if we are
> > > correct, we can directly convert to an effect size (pearson's r).
> > > However, the odds ratios in question are presented as two models, one
> > > with more covariates than the other. Is there a particular model you
> > > would suggest including (see below)?
> > >
> > > Best,
> > > Rebecca
> > >
> > > *Dr Rebecca Hall | Research Associate*
> > > Department of Psychology | Lancaster University