Lukasz Stasielowicz
Osnabr?ck University
Institute for Psychology
Research methods, psychological assessment, and evaluation
Seminarstra?e 20
49074 Osnabr?ck (Germany)
On 19.10.2022 12:00, r-sig-meta-analysis-request at r-project.org wrote:
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> 1. predictors of longitudinal outcomes (Catia Oliveira)
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> ----------------------------------------------------------------------
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> Message: 1
> Date: Tue, 18 Oct 2022 19:53:40 +0000
> From: Catia Oliveira <catia.oliveira at york.ac.uk>
> To: R meta <r-sig-meta-analysis at r-project.org>
> Subject: [R-meta] predictors of longitudinal outcomes
> Message-ID:
> <CACw+Tff0YnRmsFK8YXqe_dqn1EB16KSD6jt+v_e-dHf4sL6_-g at 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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> End of R-sig-meta-analysis Digest, Vol 65, Issue 11
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