[R-meta] Question on meta-analytic analysis of means

[Gladys Barragan-Jason]

Dear Wolfgang,

Thank you so much for your response—and for the POMP reference!

I have a quick follow-up question regarding how best to visualize the
results.

Since I am :

(1) using the rescaled means (POMP values)

(2) putting them into escalc(measure="MN"..)

(3) and then I'm doing : m1 <- rma.mv( yi, vi, mods = ~ age_mean +gender +
region , random = list(~ 1 | study_id,    ~ 1 | scale), method = "REML",
data = hnc_data).

Would you recommend primarily plotting the observed effect sizes (e.g., a
forest plot of the means), or focusing instead on the model estimates
(e.g., coefficients from the meta-regression)?

More generally, in this type of meta-analysis of means, what would you
consider the most informative way to present the results?

Thank you again for your help!

Best,
Gladys

Le mar. 24 mars 2026 à 11:17, Viechtbauer, Wolfgang (NP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> a écrit :

> Dear Gladys,
>
> As long as the means are numerically comparable, one can also meta-analyze
> means. Nothing wrong with that and this is also a meta-analysis, no need to
> use some other term.
>
> Just to put a bit of context on this: A proportion is the mean of a
> dichotomous variable. There are lots of meta-analyses of proportions (not
> differences / ratios thereof). Think about the meta-analysis of
> prevalences, the meta-analysis of sensitivity or specificity in diagnostic
> studies, the meta-analysis of the risk of side-effects, and so on. All of
> these can be thought of as meta-analyses of means.
>
> The difference here is that the response variable of interest (HNC) is
> measured not as a dichtomous variable, but as a quantitative one. If all
> studies used the same scale/measure for HNC, then you could directly
> meta-analyze the means. But since you mentioned rescaling, that doesn't
> seem to be the case. So I assume you did the following:
>
> rescaled mean = (mean - minimum-possible-score) / (maximum-possible-score
> - minimum-possible-score)
>
> which gives you a value between 0 and 1. Note that the minimum and maximum
> possible scores must be based on the possible range of scores on the
> scale/measure, not the minimum and maximum observed in the sample.
>
> One additional step involves the standard deviation. The reported SD must
> also be rescaled with:
>
> rescaled SD = SD / (maximum-possible-score - minimum-possible-score)
>
> And these you can then stick into escalc():
>
> escalc(measure="MN", mi=<rescaled means>, sdi=<rescaled SDs>, ni=<sample
> sizes>)
>
> and proceed as you have done.
>
> By the way, the rescaled means above are sometimes called 'POMP'
> (percent/proportion of maximum possible score) values:
>
> Cohen, P., Cohen, J., Aiken, L. S., & West, S. G. (1999). The problem of
> units and the circumstance for POMP. Multivariate Behavioral Research,
> 34(3), 315-346. https://doi.org/10.1207/S15327906MBR3403_2
>
> Best,
> Wolfgang
>
> > -----Original Message-----
> > From: R-sig-meta-analysis <r-sig-meta-analysis-bounces using r-project.org>
> On Behalf
> > Of Gladys Barragan-Jason via R-sig-meta-analysis
> > Sent: Tuesday, March 24, 2026 08:59
> > To: R meta <r-sig-meta-analysis using r-project.org>
> > Cc: Gladys Barragan-Jason <gladou86 using gmail.com>
> > Subject: [R-meta] Question on meta-analytic analysis of means
> >
> > Dear meta community,
> >
> > I am currently working on a meta-analysis (following PRISMA guidelines)
> > examining cultural and developmental variations in human–nature
> connectedness
> > (HNC). However, I am facing a methodological issue: most of the
> literature does
> > not report direct comparisons (e.g., children vs. adults, or
> country-to-country
> > contrasts), and therefore I do not have conventional effect sizes (e.g.,
> > standardized mean differences or correlations). Instead, I extracted
> descriptive
> > statistics from each study, including mean HNC, standard deviation,
> sample size,
> > mean age, percentage of female participants, country, region, and type
> of HNC
> > scale. My idea was to treat the (rescaled) mean HNC values as the
> outcome and
> > examine variation across studies using meta-analytic models, but I am
> unsure
> > whether this is an appropriate approach given the absence of explicit
> > comparative effect sizes.
> >
> > In terms of analysis, I first cleaned and harmonized the dataset (numeric
> > conversion, country harmonization, etc.), and rescaled HNC scores to a
> common
> > metric based on scale ranges (between 0 and 1). I then computed sampling
> > variances using escalc(measure = "MN") in metafor, effectively treating
> each
> > study’s mean as an effect size. I fitted multilevel meta-analytic models
> > (rma.mv) with study ID and scale as random effects, and included
> > moderators such as age, gender, region, and scale type. I also explored
> > publication bias (funnel plots, Egger test) and conducted moderator
> analyses
> > (including societal indicators like SDG index and biodiversity
> intactness).
> >
> > My main question is whether this strategy—meta-analyzing means using
> measure =
> > "MN" and modeling moderators—is methodologically ok in this context, or
> whether
> > I am misusing meta-analytic tools. Should this instead be framed as a
> different
> > type of analysis (e.g., meta-regression of descriptive outcomes, or a
> multilevel
> > modeling approach rather than meta-analysis)? Are there recommended
> alternatives
> > when effect sizes are not directly available, particularly for
> cross-cultural
> > and developmental comparisons? Any guidance or references would be
> greatly
> > appreciated.
> >
> > Thank you very much for your time and help.
> >
> > Best regards,
> > Gladys
> >
> > --
> > ------------------------------------------
> > Gladys Barragan-Jason,
> > PhD. https://sites.google.com/view/gladysbarraganjason/home /
> https://sites.goog
> > le.com/view/frgladysbarragan-jason/accueil
> > Chargée de recherche, CRCN
> > Station d'Ecologie Théorique et Expérimentale (SETE)
> > Centre National de la Recherche Scientifique (CNRS)
> > 2 route du CNRS, 09200 Moulis
> > Equipe LINKING
> > Groupe de recherche RISE (Recherche Interdisciplinaire pour la
> Soutenabilité
> > Environnementale)
> > Coordinatrice du réseau ETHNOECO
> > 07 72 07 93 31
>


-- 

------------------------------------------

Gladys Barragan-Jason, PhD.  Website
<https://sites.google.com/view/gladysbarraganjason/home> / Site web
<https://sites.google.com/view/frgladysbarragan-jason/accueil>

Chargée de recherche, CRCN

Station d'Ecologie Théorique et Expérimentale (SETE)

Centre National de la Recherche Scientifique (CNRS)

2 route du CNRS, 09200 Moulis

Equipe LINKING

Groupe de recherche RISE (Recherche Interdisciplinaire pour la
Soutenabilité Environnementale)

Coordinatrice du réseau ETHNOECO

07 72 07 93 31

[image: image.png][image: image.png]

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