[R-meta] Coding multi-measure correlational studies for multilevel meta-analysis

[Yuhang Hu]

Hello Experts,

I'm collecting the correlations between 8 variables from several studies.
If a study has used a single measure for all these 8 variables, I will need
28 rows (assuming no missing) to capture all those correlations i.e.,
var1.var2 = combn(1:8, 2, FUN=\(i)paste(i,collapse = ".")):

    study   ri   var1.var2
1)  1        .1   1.2
 ...
28) 1       .2    7.8

But if a study has used, say, two measures (e.g., 1, 2) for two of those 8
variables (e.g., variables "1" and "2" in 'var1.var2'), then, I wonder how
**best** to capture the additional 13 correlations arising due to the
additional measure used for "1" and "2" in that study in my data for
multilevel modeling purposes?

One approach might be to add a single column called, say "measure" to add
just those additional rows in that multi-measure study:

    study   ri   var1.var2  measure
1)   1       .1    1.2
 ...
6)   1       .6     1.7           1
7)   1       .4     1.7           2
...
12)  1       .8     2.7          1
13)  1       .7     2.7          2
...

But this looks messy. For instance, what should be the value of "measure"
for the var1.var2 rows that have used a single measure (e.g., var1.var2 ==
1.2)? And can "measure" coded this way be used in the random part of the
model (metafor::rma.mv)?

Thanks,
Yuhang

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