\title{studies on the relationship between the competitive state anxiety inventory-2 and sport performance}
\description{results from 10 studies on the relationship between the competitive state anxiety inventory-2 (csai-2) and sport performance.}
\bold{study} \tab \code{numeric}   \tab study number \cr
\bold{ni}    \tab \code{numeric}   \tab sample size \cr
\bold{sport} \tab \code{character} \tab type of sport (t = team sport, i = individual sport) \cr
\bold{ri}    \tab \code{numeric}   \tab correlation coefficient \cr
\bold{var1}  \tab \code{character} \tab variable 1 of the correlation coefficient (see \squote{details}) \cr
\bold{var2}  \tab \code{character} \tab variable 2 of the correlation coefficient (see \squote{details})
the 10 studies included in this dataset are a subset of the studies included in the meta-analysis by craft et al. (2003) on the relationship between the competitive state anxiety inventory-2 (csai-2) and sport performance.
the csai-2 has three subscales: cognitive anxiety (\code{acog}), somatic anxiety (\code{asom}), and self-confidence (\code{conf}). the studies included in this dataset administered the csai-2 prior to some sport competition and then measured sport performance based on the competition. most studies provided all 6 correlations (3 for the correlations among the 3 subscales and 3 for the correlations between the subscales and sport performance), but 2 studies (with study numbers 6 and 17) only provided a subset.
becker, b. j., & aloe, a. m. (2019). model-based meta-analysis and related approaches. in h. cooper, l. v. hedges, & j. c. valentine (eds.), \emph{the handbook of research synthesis and meta-analysis} (3nd ed., pp. 339--363). new york: russell sage foundation.
craft, l. l., magyar, t. m., becker, b. j., & feltz, d. l. (2003). the relationship between the competitive state anxiety inventory-2 and sport performance: a meta-analysis. \emph{journal of sport and exercise psychology}, \bold{25}(1), 44--65. \verb{https://doi.org/10.1123/jsep.25.1.44}
### construct dataset and var-cov matrix of the correlations
tmp <- rcalc(ri ~ var1 + var2 | study, ni=ni, data=dat)
res <- rma.mv(yi, v, mods = ~ 0 + var1.var2, random = ~ var1.var2 | study, struct="un", data=dat)
psychology, correlation coefficients, multivariate models