[R-meta] predictors of longitudinal outcomes

[Catia Oliveira]

Dear Lukasz,

Thank you for your suggestion. That is what I was thinking initially, but
was trying to come up with a simpler solution but also did not think it was
satisfactory.

Best wishes,

Catia

On Wed, 19 Oct 2022 at 14:25, Lukasz Stasielowicz <
lukasz.stasielowicz using uni-osnabrueck.de> wrote:

> 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
> > Message-ID:
> >       <
> CACw+Tff0YnRmsFK8YXqe_dqn1EB16KSD6jt+v_e-dHf4sL6_-g using mail.gmail.com>
> > Content-Type: text/plain; charset="utf-8"
> >
> > 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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