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Metafor and forest(); not showing 'ilab' and text

I did read the help(rma.mv) and I also had look at the analysis by
Konstantopoulos (2011) in the past few days. You have to apologize me
but is the first meta analysis I'm trying to carry on, it is the first
I'm working on R and moreover the terminology here is somehow
different (and confusing) with respect to the terminology I was used
in panel data analysis.

It looks to me - correct me if I'm wrong - that a model such:

     rma.mv(pc, var, random = ~ 1 | author, data=codebook)

or

     rma.mv(pc, var, random = ~ 1 | author, mods = ~ pub + SIMiv, data=codebook)

it is a varying intercept model (using Gelman-Hill Yi = Aji+BXi+Ei).
Why do you say that "Using "random = ~ 1 | author" is likely to be
insufficient. You also need to add random effects at the observation
level"? Could you please walk me through what you mean by that?

I'll (try to) explain to you what I'm doing here so you might be able
to help me out.
I'm carrying on a meta-analysis (and ultimately, few meta-regressions)
of the relationship between firm performances and bank-firm
relationship. Since I have different proxies for the latter, I
computed as effect size a raw partial correlation, a continuous
Fisher?s z-score and the one-tail p-value as a continuous
interpretation of the direction and the significance of an effect
size.

The number of studies in my meta-analysis is 29, but most of them have
multiple cases so, ultimately, I have 98 different cases. All of the
29 studies have repeated yearly observation in the same country for
different time span; let's say one from 1985 to 1991 in Spain, one
from 1987 to 1999 in China, one from 1981 to 2002 in the US and so on.
Even in case in which two different study investigates the same
country and some of the years in the time span overlaps I'm sure that
their population is drawn from different samples.

My idea was too confront what in econometrics is called a study-fixed
effect (or author fixed effect since authors have no more than one
study in my analysis), that is:

       rma(pc, var, mods = ~ I(study), data=codebook)

With a multilevel model, in order to account for the fact that
observation from the same study are not independent.

May I have your opinion on what I'm trying to do here guys?
Do you think I should take into account also a autoregressive
structure over time and correct that with struct="HAR" ?

Sorry for the long mail, I wanted to explain as better as possible,
and thank you again for your help, It is incredibly appreciated.

Marco

On 31 August 2015 at 19:28, Viechtbauer Wolfgang (STAT)
<wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
Message-ID: <CALxyAHRzYb1qfOLycJAFeKg88prZeqGFX7ZJUq8ZQFo+K64vSw@mail.gmail.com>
In-Reply-To: <077E31A57DA26E46AB0D493C9966AC730F1ADBC6F2@UM-MAIL4112.unimaas.nl>