\name{dat.metap}
\doctype{data}
\alias{dat.metap}
\title{example data for the meta-analysis of significance values}
\description{a collection of datasets for illustrating the meta-analysis of significance values (i.e., methods for combining p-values from tests of significance).}
\usage{
dat.metap
}
\format{
a list with the following elements:
\describe{
\item{\code{beckerp}}{a numeric vector with 5 hypothetical \eqn{p}-values}
\item{\code{cholest}}{a data frame with 34 observations on the following 5 variables:
\tabular{lll}{
\bold{ntreat}   \tab \code{numeric} \tab number of patients in the treated group \cr
\bold{ncontrol} \tab \code{numeric} \tab number of patients in the control group \cr
\bold{dtreat}   \tab \code{numeric} \tab number of deaths in the treated group \cr
\bold{dcontrol} \tab \code{numeric} \tab number of deaths in the control group \cr
\bold{p}        \tab \code{numeric} \tab one-sided \eqn{p}-values \cr
}}
\item{\code{edgington}}{a vector of with 7 hypothetical \eqn{p}-values}
\item{\code{mourning}}{a data frame with 9 observations on the following 3 variables:
\tabular{lll}{
\bold{stance} \tab \code{character} \tab character variable with levels \code{no stand}, \code{opponent}, \code{supporter} \cr
\bold{grade}  \tab \code{character} \tab character variable with levels \code{g11-12}, \code{g7-8}, \code{g9-10} \cr
\bold{p}      \tab \code{numeric}   \tab \eqn{p}-values \cr
}}
\item{\code{naep}}{a data frame with 34 observations on the following 2 variables:
\tabular{lll}{
\bold{state} \tab \code{character} \tab character variable with two-letter us state names \cr
\bold{p}     \tab \code{numeric}   \tab \eqn{p}-values \cr
}}
\item{\code{rosenthal}}{a data frame with 5 observations on the following 3 variables:
\tabular{lll}{
\bold{t}  \tab \code{numeric} \tab t-statistics \cr
\bold{df} \tab \code{numeric} \tab degrees of freedom \cr
\bold{p}  \tab \code{numeric} \tab one-sided \eqn{p}-values \cr
}}
\item{\code{teachexpect}}{a vector of 19 \eqn{p}-values}
\item{\code{validity}}{
a data frame with 20 observations on the following 3 variables:
\tabular{lll}{
\bold{n} \tab \code{numeric} \tab sample sizes \cr
\bold{r} \tab \code{numeric} \tab correlation coefficients \cr
\bold{p} \tab \code{numeric} \tab one-sided \eqn{p}-values \cr
}}
\item{\code{zhang}}{a data frame with 22 observations on the following 11 variables:
\tabular{lll}{
\bold{study}  \tab \code{character} \tab study names \cr
\bold{smd}    \tab \code{numeric}   \tab standardized mean differences \cr
\bold{lo}     \tab \code{numeric}   \tab lower confidence interval limits \cr
\bold{hi}     \tab \code{numeric}   \tab upper confidence interval limits \cr
\bold{ntreat} \tab \code{numeric}   \tab treated group sample sizes \cr
\bold{ncont}  \tab \code{numeric}   \tab control group sample sizes \cr
\bold{n}      \tab \code{numeric}   \tab total sample sizes \cr
\bold{phase}  \tab \code{factor}    \tab phase the patients were in: acute, healing, healed \cr
\bold{sd}     \tab \code{numeric}   \tab the calculated standard deviations \cr
\bold{z}      \tab \code{numeric}   \tab the calculated z-values \cr
\bold{p}      \tab \code{numeric}   \tab one-sided \eqn{p}-values \cr
}}
}
}
\details{
\describe{
\item{\code{beckerp}}{hypothetical \eqn{p}-values from becker (1994).}
\item{\code{cholest}}{trials of interventions for cholesterol lowering from sutton et al. (2000).}
\item{\code{edgington}}{hypothetical \eqn{p}-values from edgington (1972).}
\item{\code{mourning}}{results from a study of mourning practices of israeli youth following the assassination of itzakh rabin from benjamini and hochberg (2000).}
\item{\code{naep}}{results of mathematical achievement scores from the national assessment of educational progress from benjamini and hochberg (2000).}
\item{\code{rosenthal}}{hypothetical example from rosenthal (1978).}
\item{\code{teachexpect}}{\eqn{p}-values from studies of the effect of manipulating teacher expectancy on student iq from becker (1994).}
\item{\code{validity}}{data from studies of validity of student ratings of their instructors from becker (1994) including correlations and sample sizes as well as \eqn{p}-values.}
\item{\code{zhang}}{data from trials of exercise training for patients with cardiovascular disease from zhang et al. (2016).}
}
}
\author{michael dewey}
\note{
the \eqn{p}-values in \code{cholest} have been re-calculated from other data given in the book and so are of higher accuracy than the ones given in the book which are only to two decimal places.
}
\references{
becker, b. j. (1994). combining significance levels. in h. cooper & l. v. hedges (eds.), \emph{the handbook of research synthesis} (pp. 215–230). new york: russell sage foundation.
benjamini, y., & hochberg, y. (2000). on the adaptive control of the false discovery rate in multiple testing with independent statistics. \emph{journal of educational and behavioral statistics}, \bold{25}(1), 60--83. \verb{https://doi.org/10.3102/10769986025001060}
edgington, e. s. (1972). an additive method for combining probability values from independent experiments. \emph{journal of psychology}, \bold{80}(2), 351-363. \verb{https://doi.org/10.1080/00223980.1972.9924813}
rosenthal, r. (1978). combining results of independent studies. \emph{psychological bulletin}, \bold{85}(1), 185--193. \verb{https://doi.org/10.1037/0033-2909.85.1.185}
sutton, a. j., abrams, k. r., jones, d. r., sheldon, t. a., & song, f. (2000). \emph{methods for meta-analysis in medical research}. chichester, uk: wiley.
zhang, y.-m., lu, y., tang, y., yang, d., wu, h.-f., bian, z.-p., xu, j.-d., gu, c.-r., wang, l.-s., & chen, x.-j. (2016). the effects of different initiation time of exercise training on left ventricular remodeling and cardiopulmonary rehabilitation in patients with left ventricular dysfunction after myocardial infarction. \emph{disability and rehabilitation}, \bold{38}(3), 268--276. \verb{https://doi.org/10.3109/09638288.2015.1036174}
}
\examples{
dat.metap
}
\keyword{datasets}
\concept{combining p-values}
\section{concepts}{
combining p-values
}