[R-meta] Negative r value for effect sizes within studies

I should add that it is entirely "possible" to encounter outcomes that
are measured in opposite directions across studies and that can be
meaningfully part of the same multivariate meta-analysis. But like I
mentioned, syncing the outcomes' directions often resolves the issue.

Here is a substantive example.

Studies that research the efficacy of teacher's feedback on students'
(English Language Learners) writing competence often focus on various
related linguistic features (e.g., verbs, articles etc.). However, due
to the debates over how these linguistic features ought to be
measured, these studies tend to measure some linguistic features "by
tradition" using the number of errors, but others using the number of
correct usage (I'm simplifying the actual measures to make this
discussion more general, but the directions of outcomes run opposite
to one another for different linguistic features).

The fact that these outcome levels are related is of great interest,
hence motivating a multivariate meta-analysis. On the other hand,
almost always these studies are multi-group to compare different types
of feedback and longitudinal to measure the durability of feedback's
effect on developing students' writing competence (to make matters
even more complex, they even use more than one comparison/control
group).

In a situation like what I described above (and more generally as
described in my previous email), if not synced, these opposing
outcomes could create a difficult situation where each study could
have a combination of positively and negatively correlated estimates
of effect size in it.

So, synching the outcome directions, at least for me, is inevitable.

Reza

[R-meta] Negative r value for effect sizes within studies

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