-----Original Message-----
From: Wolf, Katrin [mailto:katrin.wolf at uni-bamberg.de]
Sent: Thursday, 13 July, 2023 8:45
To: Viechtbauer, Wolfgang (NP); R Special Interest Group for Meta-Analysis
Subject: AW: Calculating effect sizes from standardized regression coefficients
in Metafor
Dear Wolfgang,
Thank you very much for your response! Please let me summarize to check if I
understood correctly. As I could also see from the correspondence with Rasheda,
there are two possible ways for dealing with regression coefficients:
1. Using escalc with "PCOR" (under consideration of t-statistics, sample size,
number of predictors and variance explained)
2. directly meta-analyze beta coefficients as effect size (yi) in one of the rma-
commands. In this case vi would be variance of beta coefficient meaning square of
SE?
(I am going to check the paper you mentioned regarding the standard errors.)
Is one of these approaches better than the other - considering the fact that I
aim at aggregating effect sizes from different kind of measures: regression
coefficients, correlation coefficients (Fisher?s r-to-z transformed correlation
coefficient) and group mean differences (standardized mean difference)?
Best,
Katrin
-----Urspr?ngliche Nachricht-----
Von: Viechtbauer, Wolfgang (NP) <wolfgang.viechtbauer at maastrichtuniversity.nl>
Gesendet: Mittwoch, 12. Juli 2023 16:57
An: R Special Interest Group for Meta-Analysis <r-sig-meta-analysis at r-
project.org>
Cc: Wolf, Katrin <katrin.wolf at uni-bamberg.de>
Betreff: RE: Calculating effect sizes from standardized regression coefficients
in Metafor
Dear Katrin,
I am not sure I fully understand your question. I think you are referring to
escalc() with measure "PCOR", which calculates partial correlation coefficients
(from things like the corresponding t-statistics of the regression coefficients),
but your phrasing (that this "calculates effect size from partial correlations")
is confusing me.
If you want to meta-analyze standardized regression coefficients and have the
corresponding standard errors, then one can of course also meta-analyze those
directly. However, note that the standard errors of standardized regression
coefficients are typically not computed in the most accurate way (i.e., the
standard errors one obtains by fitting a regression model to standardized
variables ignore that the variances used to standardize the variables are
estimated). See, for example:
Jones, J. A., & Waller, N. G. (2013). Computing confidence intervals for
standardized regression coefficients. Psychological Methods, 18(4), 435-453.
https://doi.org/10.1037/a0033269
If you have the full correlation matrix of the variables in each regression
model, one can compute more appropriate standard errors, but this is unlikely to
be the case in practice.
Best,
Wolfgang
-----Original Message-----
From: R-sig-meta-analysis
[mailto:r-sig-meta-analysis-bounces at r-project.org] On Behalf Of Wolf,
Katrin via R-sig-meta-analysis
Sent: Tuesday, 11 July, 2023 12:33
To: r-sig-meta-analysis at r-project.org
Cc: Wolf, Katrin
Subject: [R-meta] Calculating effect sizes from standardized regression
coefficients in Metafor
Dear colleagues,
I am currently struggling with dealing with standardized regression
coefficients (as indicator of the relationship between two variables of
interest) in my meta- analysis with Metafor. Due to literature
research, standardized regression coefficients can be used for
meta-analysis when corresponding standard errors are also taken into
account. Due to Metafor manual from 2023, it is possible to calculate
effect size from partial correlations under consideration of t-
statistics, sample size, number of predictors in regression model and R^2. Do I
interpret correctly that this is another approach?
I am sure there is a lot of experience with handling beta weights in
Metafor. I would appreciate any information on this topic.
Kind regards,
Katrin
---
Dr. Katrin Wolf, Dipl.-Psych.
Wissenschaftliche Mitarbeiterin
Otto-Friedrich-Universit t Bamberg
Lehrstuhl Fr hkindliche Bildung und Erziehung
96045 Bamberg