[R-meta] predictors of longitudinal outcomes

[Lukasz Stasielowicz]

Dear Catia,

Wouldn't it be more straightforward to address such research questions 
using meta-analytic structural equation modeling? One could connect 
antecedents (e.g., gender, SES) with skills at T2 or even T3 if there 
are some studies with multiple measurement occasions. One could also 
compare different models to examine the relevance of specific predictors.


Some resources in case you're not familiar with this approach:

Shiny app: https://sjak.shinyapps.io/webMASEM/

Video demonstration: https://www.youtube.com/watch?v=0v-CdNLa_eo

Article: Jak, S., Li, H., Kolbe, L., de Jonge, H., & Cheung, M. W. L. 
(2021). Meta‐analytic structural equation modeling made easy: A tutorial 
and web application for one‐stage MASEM. Research synthesis methods, 
12(5), 590-606. https://doi.org/10.1002/jrsm.1498

The shiny app is based on the metaSEM package, which enables further 
analyses within R: 
https://cran.r-project.org/web/packages/metaSEM/vignettes/Examples.html



Best,
Lukasz
-- 
Lukasz Stasielowicz
Osnabrück University
Institute for Psychology
Research methods, psychological assessment, and evaluation
Seminarstraße 20
49074 Osnabrück (Germany)

On 19.10.2022 12:00, r-sig-meta-analysis-request using r-project.org wrote:
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>     1. predictors of longitudinal outcomes (Catia Oliveira)
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> Message: 1
> Date: Tue, 18 Oct 2022 19:53:40 +0000
> From: Catia Oliveira <catia.oliveira using york.ac.uk>
> To: R meta <r-sig-meta-analysis using r-project.org>
> Subject: [R-meta] predictors of longitudinal outcomes
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> 
> Dear all,
> 
> I hope this email finds you well.
> I am interested in analysing longitudinal studies where a particular group
> of individuals (diagnosed at time 1) is followed across time and then have
> their skills measured at some later date (follow-up - time 2). I am not
> interested in estimating the difference in skills between time points, but
> instead, I want to determine which factors measured at time 1 (e.g.,
> gender, age) predict their skills at time 2. Assuming the models would be
> regressions where the outcome variable at time 2 is predicted by each
> factor at time 1 independently, could we use cohen's f as the effect size
> for the meta-analysis and then run a meta-regression to see which factors
> explain the most variance and which combinations lead to more explanatory
> power? (e.g., voc ~ gender + SES). If this is completely wrong, could you
> please point me to a study that has examined similar questions?
> 
> The dataset I am imagining would look something like this:
> 
> Study | Moderator | cohen's f | Outcome
> 
> S1 | gender | .23  | voc
> 
> S1 | SES | .12 | voc
> 
> S2| gender | .02 | voc
> 
> 
> Thank you!
> 
> 	[[alternative HTML version deleted]]
> 
> 
> 
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