[R-meta] Combining studies reporting effects at different level of analysis/aggregation

[Viechtbauer, Wolfgang (SP)]

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Yes, you could also 'guestimate' the ICC and use that (and then do a sensitivity analysis). Even if you do the correction, I would still recommend to include study type as a moderator in the analyses.

Best,
Wolfgang

-----Original Message-----
From: F S [mailto:crpt.fs using gmail.com] 
Sent: Thursday, 11 October, 2018 18:47
To: Viechtbauer, Wolfgang (SP)
Subject: Re: [R-meta] Combining studies reporting effects at different level of analysis/aggregation

Hello Wolfgang,

Thank you for your helpful answer. I'm afraid none of the studies in question report the ICC, so I guess a precise correction for the inflated d won't be possible. However, would it be sensible to instead impute a value for rho and perform the adjustment for the design effect using that value? Ideally, one would impute ICC values lifted from studies with a similar type of aggregation and similar measures, but I suppose one could also perform the correction for a range of plausible values of rho and evaluate the impact on the overall results via sensitivity analysis. What do you think?

Thank you very much,
Fabian

On Fri, Oct 5, 2018 at 1:17 PM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
Hi Fabian,

I don't think you have received any responses to your question so far, so let me take a stab here.

You did not say what kind of effect size / outcome measure you want to use for your meta-analysis, but if it something like a standardized mean difference ('d-values'), then what you describe is definitely an issue. The means (i.e., the averaged individual responses within groups) will have a lower variance than the responses from individuals, leading to higher d-values in studies reporting statistics based on group-level means. That makes d-values from the two types of studies pretty much non-comparable. At the very least, you should include study type as a moderator in all of the analyses.

If you know the ICC of the responses within groups, then one could correct for the inflation of the d-values based on the 'variance inflation factor' or 'design effect'. In essence, d-values from 'group studies' are then adjusted by the multiplicative factor

sqrt((1+(n-1)*rho)/n),

where n is the (average) group size and rho is the ICC. That should make the d-values from the two types of studies more directly comparable. The sampling variance of a d-value from a 'group study' also needs to be adjusted based on the square of the multiplicative factor (this ignores the uncertainty in the estimated value of the ICC, but ignoring sources of uncertainty when estimating sampling variances happens all the time).

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On Behalf Of F S
Sent: Tuesday, 18 September, 2018 20:47
To: r-sig-meta-analysis using r-project.org
Subject: [R-meta] Combining studies reporting effects at different level of analysis/aggregation

I am currently working on a meta-analysis in the social sciences. All
studies measured the relevant outcome at the level of participants, but a
few studies aggregated at a higher level of analysis (e.g., groups) before
statistics were computed. Can these studies be meta-analyzed together?

More detail: The relevant outcome is a continuous measure, assessed at the
level of individual participants. The majority of studies report
statistical effects computed at the level of participants. However, in a
number of studies, random assignment occurred not at the participant level,
but at the level of groups (e.g., dyads, 3-person groups, classrooms).
Although each of these studies did assess the outcome at the participant
level, just like the other studies, statistical effects are computed at the
group level. As such, they are different from cluster-randomized studies,
in which randomization occurs at the group level but results are reported
at the individual level. By contrast, the studies in question averaged
individual responses within groups before computing effects with group as
the unit of analysis.

I'm not sure I can include these studies in my meta-analysis, but could not
find much work on this question. Ostroff and Harrison (1999) focused
specifically on correlations computed at different levels of analysis, and
they make a strong case against combining ES from such studies: "the
obtained meta-analytic ρ̂  may not be interpretable as an estimate of any
population parameter because authors have cumulated studies in which
samples were drawn from different levels" (p. 267).

Can I can include these studies reporting effects from aggregated
observations, and if so, are there specific procedures to do so? (I'm
planning to use rma.mv in metafor, with cluster-robust variance estimates,
using clubSandwich.)

Many thanks!
Fabian