[R-meta] predictors of longitudinal outcomes

[Catia Oliveira]

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!

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