weights in mixed modelling

Hi everyone,

I?m looking for advice and/or information about using weights in mixed
modelling. My colleagues and I are conducting an analysis and we?ve
attempted to use weights to solve two issues. As is likely obvious, I am
not a statistician, so please excuse my ignorance! We?re using data
gathered from several hundred studies. The studies represent a spectrum of
quality and robustness and therefore we have created an standardized ?index
of study quality? to rank each of the data sources. It was our (perhaps
dubious) understanding that we could use such an index as model weights in
our analysis. There are also instances in which studies? presented data in
aggregate, and therefore we had to break data into multiple observations.
We had hoped weights could mitigate any issues arising from
pseudoreplication. For this, we created an ?observation weight?, in which
each independent observation was assigned a weight of 1 and observations
which were broken into multiple observations were given a weight = 1/(# of
observations). We thought combining the ?index of study qualities? with the
?observation weight? via multiplication could give us a composite weight?
The model we are using is a Beta-distributed mixed effects model, fitted
using  ?glmmTMB?.

If you have any advice or suggestions or relevant reading materials, I
would greatly appreciate it.

Thank you in advance for your time and patience,

All the Best,

-Alex B.
~~~~~~~~~~~~~~~~~~~~~~~~~~~
J. Alex Baecher (he, him, his)
PhD student, Research Assistant
School of Natural Resources and Environment
University of Florida
354 Newins-Ziegler Hall
Gainesville, Florida 34611 (USA)
--
phone: ?(352) 575-0454
e-mail: jbaecher at ufl.edu<mailto:jbaecher at ufl.edu>
website: www.alexbaecher.com<http://www.alexbaecher.com/>
~~~~~~~~~~~~~~~~~~~~~~~~~~~




        [[alternative HTML version deleted]]