_______________
Dr. Daniel Noble
Senior Lecturer
Division of Ecology and Evolution (office?W317),
Research School of Biology,
College of Science,
Robertson Building,
46 Sullivans Creek Road,
The Australian National University
Canberra, ACT 2600
Australia
?T +61 02 6125 0423
M +61 430 290 053
daniel.noble at anu.edu.au
Noble Lab Webpage: www.nobledan.com
RSB: https://biology.anu.edu.au
CRICOS Provider #00120C
On 12 Feb 2022, at 12:24 pm, James Pustejovsky <jepusto at gmail.com> wrote:
Hi meta-analysis folks,
I have a kind of vague question about something I've run across a few
times. There are some (perhaps rare) situations where investigators are
interested in the absolute magnitude of an effect but where the sign or
direction of the effect is arbitrary or not meaningful. Consequently,
meta-analysts of such effects might like to work with _unsigned_ effect
size estimates rather than the estimates that describe both magnitude and
direction. However, taking the absolute value of an estimate changes its
sampling distribution--potentially quite drastically!--in a way that would
make conventional meta-analytic models
(fixed/common/random/multi-level/multi-variate) perform rather poorly.
Does anyone know of work on methods for synthesis of unsigned effects, that
actually account for the consequences of using absolute effect size
estimates?
James