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

[Viechtbauer, Wolfgang (NP)]

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