[R-meta] To include or not include time in rma.mv
yh342 @end|ng |rom n@u@edu
Tue Sep 6 20:41:57 CEST 2022
Thank you very much for the clarification.
I'm assuming that you guys excluded the pretreatment effects mainly because
your study pool consisted only of true experiments (right?).
I might be wrong, but random assignment in single experiments could still
suffer from bad luck and can equalize the groups only on average given
So, do you advise including a "time" interaction overall?
Thank you so much for your time.
On Tue, Sep 6, 2022 at 8:04 AM James Pustejovsky <jepusto using gmail.com> wrote:
> Hi Yuhang,
> As one of the authors of the second study you mentioned, I think you may
> be misinterpreting our analysis. My understanding is that the data that we
> used to develop our examples did not include standardized mean differences
> prior to treatment. All of the included effect sizes were for
> post-treatment effects (i.e., `postwks >= 0`).
> I can't say for certain, but I suspect that the same thing holds for the
> Williams et al. meta-analysis (the first study you mentioned). If you look
> at their Table 2, the "Outcome timing" variable ranges from "midstream
> during intervention" to "follow-up post-test," so there don't appear to be
> any pre-treatment effect sizes included in the analysis.
> If the data did include pre-test effect sizes, then I fully agree that m1
> would blur the interpretation of the treatment effects.
> On Mon, Sep 5, 2022 at 7:36 PM Yuhang Hu <yh342 using nau.edu> wrote:
>> Hello All,
>> I'm considering two candidate fixed-effects structures (cat_mod
>> = categorical moderator) to meta-analyze a group of longitudinal
>> studies (pre-test and follow-ups):
>> m1 = rma.mv(yi ~ 0 + cat_mod + covariates, random = ~ 1 | study/effect)
>> m2 = rma.mv(yi ~ 0 + cat_mod*time + covariates, random = ~ 1 |
>> In several authoritative meta-analyses of longitudinal studies such as
>> ***https://doi.org/10.1080/19345747.2021.2009072*** and
>> ***https://doi.org/10.1007/s11121-021-01246-3***, I noticed the authors
>> used "m1" over "m2".
>> But I was wondering, wouldn't, ignoring "time" (as in "m1") in
>> the fixed-effects structure mix up the pre-test and follow-up effect
>> sizes together which can blur our understanding of the treatment effect?
>> Your guidance is highly appreciated,
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Yuhang Hu (She/Her/Hers)
Ph.D. Student in Applied Linguistics
Department of English
Northern Arizona University
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