[R-meta] Question Re: Correcting for Statistical Artifacts

Yefeng Yang ye|eng@y@ng1 @end|ng |rom un@w@edu@@u
Fri May 26 04:01:22 CEST 2023

Dear Tori

The method mentioned is called bare-bones meta-analysis, which is a common method in the psychological meta-analysis (it is also called psychometric meta-analysis or called Hunter and Schmidt to remind the contributors of this method ).  The core idea is you need to correct for various artifacts in the effect size estimates (mostly in correlation but can be extended to other effect size measures). This includes, for example, using the reliability coefficient to account for measurement error (as suggested by your reviewer), and range restrictions. Schmidt and Hunter�s (2015) is the best reference for this. metafor can implement this with some tricks., But as far as I know there is a dedicated R package to do this, although I have not used it before - psychmeta package.


From: R-sig-meta-analysis <r-sig-meta-analysis-bounces using r-project.org> on behalf of Tori Pe�a via R-sig-meta-analysis <r-sig-meta-analysis using r-project.org>
Sent: Friday, 26 May 2023 1:51
To: r-sig-meta-analysis using r-project.org <r-sig-meta-analysis using r-project.org>
Cc: Tori Pe�a <Tori.Pena using stonybrook.edu>
Subject: [R-meta] Question Re: Correcting for Statistical Artifacts

Hello Listserv -

I'm sending this question out again in case it slipped through the cracks:

I am working on a multilevel meta-analysis and a reviewer asked us to
correct for statistical artifacts (e.g., measurement error).  The reviewer
cited Wiernik & Dahlke (2020) which includes some code using the metafor

I am still unclear on a couple of things related to these analyses.  First,
can one apply these analyses to nested models?  Second, one needs observed
selection-effect *u* ratios to conduct these models but I am not sure how
to get these values to consequently conduct the models.  I copied and
pasted example code from Wiernik & Dahlke (2020) below for reference.  The
"ux" and "uy" values are the selection-effect *u *ratios.

ma_results_r <-
  ma_r(ma_method = "ad",
  rxyi = rxyi,
  n = n,
  rxx = rxxi,
  ryy = ryyi,
  ux = ux,
  uy = uy,
  correct_rxx = TRUE,
  correct_ryy = TRUE,
  correct_rr_x = TRUE,
  correct_rr_y = TRUE,
  indirect_rr_x = TRUE,
  indirect_rr_y = TRUE,
  data = data_r_bvirr)

Any guidance would be greatly appreciated!  Thank you in advance for your
time and energy on addressing this question.  I especially wanted to get
input from this group since these analyses are new to me.


*Tori Pe�a, Ph.D. *(she/her/ella)
Cognitive Psychology
Dept. of Psychology
Stony Brook University
Stony Brook, NY 11790-2500
[image: Stony Brook University logo]

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