[R-meta] query re: multi-level analysis in metafor with missing standard errors

Jennifer Oser o@er @end|ng |rom po@t@bgu@@c@||
Tue Sep 29 18:13:17 CEST 2020


Hi all,

My co-authors and I are conducting a multi-level meta-analysis, and I write
with a question about calculating the sampling variance to use in the rma.mv
argument. We followed the approach of calculating the sampling variance (v)
by squaring the standard error, as documented in "Doing Meta-Analysis in R"
here:
https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/fitting-a-three-level-model.html.
However, most of the regressions in the studies that meet our inclusion
criteria report on standardized coefficients without reporting standard
errors, and therefore cannot be included in the multi-level analysis using
this approach.

Our question is therefore: Is there a way to conduct a multi-level analysis
that calculates the relevant sampling variance based on regression outputs
that report standardized coefficients but do not report standard errors? We
know that it is possible to do conversions for an effect size in
non-multi-level studies, but we have not yet seen documentation on how to
do this sort of conversion for multi-level meta-analysis based on
regression outputs.

If the answer is a definitive “No, this is not possible (at least at this
time)” – this would also be a useful answer, as we have conducted
vote-counting tests in our paper, and can report on those results along
with the multi-level findings from the smaller number of effects that do
include the necessary information.

If the answer is “Yes, this is possible but it’s complicated…” - any advice
and/or references for addressing this issue would be greatly appreciated,
and we are happy to provide more information on the analysis as useful. We
are in the final stages of revising a paper that has received very positive
feedback in various conferences (on the topic of political efficacy and
online political participation), and would be grateful for leads that would
help us conduct as rigorous and comprehensive an analysis as possible.

Best wishes,
Jenny


*Dr. Jennifer Oser*

Department of Politics & Government | Ben-Gurion University of the Negev

Department website
<http://in.bgu.ac.il/en/humsos/politics/Pages/staff/jennifer_oser.aspx>
| Research
and teaching website <http://www.jenniferoser.com/>

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