\description{batting averages in 2014 for 10 baseball teams.}
\bold{team}     \tab \code{character} \tab baseball team \cr
\bold{division} \tab \code{factor}    \tab division \cr
\bold{mean}     \tab \code{numeric}   \tab batting average (team sample mean) \cr
\bold{se}       \tab \code{numeric}   \tab team standard error \cr
\bold{batters}  \tab \code{integer}   \tab number of batters
meeker, hahn, and escobar (2017) considered an example application concerning batting averages from 10 baseball teams during 2014. the data (from chapter 17) consist of sample means (batting averages) and corresponding standard errors for each team's batters who had at least 300 \emph{at-bats}. baseball teams are associated with one of two divisions, namely \emph{american league central (al-c)} or \emph{national league central (nl-c)}.
meeker, w. q. , hahn, g. j., & escobar, l. a. (2017).  \emph{statistical intervals: a guide for practitioners and researchers} (2nd ed.). hoboken, nj: john wiley and sons.
forestplot(es, zero=na, title="baseball batting averages (dat.meeker2017)")
# distinguishing between al-c and nl-c divisions
# (using improper uniform priors for all 3 parameters)
# show more summary stats for some estimates

\title{the non-persuasive power of a brain image}
\description{results from studies exploring how a superfluous fmri brain image influences the persuasiveness of a scientific claim.}
\bold{study}             \tab \code{character} \tab name of the study: citation - experiment - subgroup \cr
\bold{no_brain_n}        \tab \code{numeric}   \tab sample size for no-brain-image condition \cr
\bold{no_brain_m}        \tab \code{numeric}   \tab mean agreement rating for no-brain-image condition \cr
\bold{no_brain_s}        \tab \code{numeric}   \tab standard deviation for no-brain-image condition \cr
\bold{brain_n}           \tab \code{numeric}   \tab sample size for brain-image condition \cr
\bold{brain_m}           \tab \code{numeric}   \tab mean agreement rating for brain-image condition \cr
\bold{brain_s}           \tab \code{numeric}   \tab standard deviation for brain-image condition \cr
\bold{included_critique} \tab \code{character} \tab \squote{critique} if article included critical commentary on conclusions, otherwise \squote{no_critique} \cr
\bold{medium}            \tab \code{character} \tab \squote{paper} if conducted in person; \squote{online} if conducted online \cr
\bold{compensation}      \tab \code{character} \tab notes on compensation provided to participants \cr
\bold{participant_pool}  \tab \code{character} \tab notes on where participants were recruited \cr
\bold{yi}                \tab \code{numeric}   \tab raw mean difference, calculated as \code{brain_m - no_brain_m} \cr
\bold{vi}                \tab \code{numeric}   \tab corresponding sampling variance \cr
the dataset contains the data from the meta-analysis by michael et al. (2013) of experiments on the persuasive power of a brain image. the meta-analysis analyzed an original study by mccabe and castel (2008) as well as 10 replication attempts conducted by the authors of the meta-analysis.
in each study, participants read an article about using brain imaging as a lie detector. the article either included a superfluous fmri image of a brain (brain) or not (no_brain). after reading the article, all participants responded to the statement \dquote{do you agree or disagree with the conclusion that brain imaging can be used as a lie detector?} on a scale from 1 (strongly disagree) to 4 (strongly agree).
the original study by mccabe and castel (2008) reported a relatively large increase in agreement due to the presence of brain images. meta-analysis of the original study with the 10 replications suggests, however, a small, possibly null effect: an estimated average raw mean difference of 0.07 points, 95\% ci [-0.00, 0.14], under a random-effects model.
in some studies, the article included a passage critiquing the primary claims made in the article; this is coded in the \code{included_critique} column for analysis as a possible moderator. note that experiment 3 by mccabe and castel (2008) was a 2x2 between subjects design: brain image presence was manipulated as well as the inclusion of a critique. the two different critique conditions are recorded as separate rows in this dataset. analysis of this dataset with metafor yields the same results (given rounding) reported in the manuscript.
michael, r. b., newman, e. j., vuorre, m., cumming, g., & garry, m. (2013). on the (non)persuasive power of a brain image. \emph{psychonomic bulletin & review}, \bold{20}(4), 720–-725. \verb{https://doi.org/10.3758/s13423-013-0391-6}
mccabe, d. p., & castel, a. d. (2008). seeing is believing: the effect of brain images on judgments of scientific reasoning. \emph{cognition}, \bold{107}(1), 343--352. \verb{https://doi.org/10.1016/j.cognition.2007.07.017}
# yi and vi are already provided, but here's how you would use escalc() to obtain
# a raw-mean difference and its variance.
# note the measure parameter is "md" for 'raw mean difference'
### meta-analysis using a random-effects model of the raw mean differences
### examine if included_critique is a potential moderator
res <- rma(yi, vi, mods = ~ included_critique, data=dat)
psychology, persuasion, raw mean differences