metaSEM (version 0.9.4)

Becker09: Ten Studies of Correlation Matrices used by Becker (2009)

Description

This data set includes ten studies on the relationships between CSAI subscales and sport behavior. The original data were used in Craft et al. (2003), whereas a subset of them were illustrated in Becker (2009).

Usage

data("Becker09")

source

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. Journal of Sport and Exercise Psychology, 25(1), 44-65.

Details

A list of data with the following structure: [object Object],[object Object],[object Object]

References

Becker, B. J. (2009). Model-based meta-analysis. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta-analysis (2nd ed., pp. 377-395). New York: Russell Sage Foundation.

Examples

data(Becker09)

#### Fixed-effects model
## First stage analysis
fixed1 <- tssem1(Becker09$data, Becker09$n, method="FEM")
summary(fixed1)

## Prepare a regression model using create.mxMatrix()
A1 <- create.mxMatrix(c(0, "0.1*Cog2Per", "0.1*SO2Per", "0.1*SC2Per",
                        0, 0, 0, 0,
                        0, 0, 0, 0,
                        0, "0.1*Cog2SC", "0.1*SO2SC",0),
                      type="Full", byrow=TRUE, ncol=4, nrow=4,
                      as.mxMatrix=FALSE)

## This step is not necessary but it is useful for inspecting the model.
dimnames(A1)[[1]] <- dimnames(A1)[[2]] <- c("Per","Cog","SO","SC")

## Display A1
A1

S1 <- create.mxMatrix(c("0.1*var_Per",
                        0, 1,
                        0, "0.1*cor", 1,
                        0, 0, 0, "0.1*var_SC"), byrow=TRUE, type="Symm",
                      as.mxMatrix=FALSE)

## This step is not necessary but it is useful for inspecting the model.
dimnames(S1)[[1]] <- dimnames(S1)[[2]] <- c("Per","Cog","SO","SC")

## Display S1
S1

## Second stage analysis
fixed2 <- tssem2(fixed1, Amatrix=A1, Smatrix=S1, diag.constraints=TRUE,
                 intervals.type="LB", model.name="TSSEM2 Becker09",
                 mx.algebras=list( Cog=mxAlgebra(Cog2SC*SC2Per, name="Cog"),
                                   SO=mxAlgebra(SO2SC*SC2Per, name="SO"),
                                   Cog_SO=mxAlgebra(Cog2SC*SC2Per+SO2SC*SC2Per,
                                   name="Cog_SO")) )
summary(fixed2)

#### Fixed-effects model: with type of sport as cluster
## First stage analysis
cluster1 <- tssem1(Becker09$data, Becker09$n, method="FEM",
                   cluster=Becker09$Type_of_sport)
summary(cluster1)

## Second stage analysis
cluster2 <- tssem2(cluster1, Amatrix=A1, Smatrix=S1, diag.constraints=TRUE,
                 intervals.type="LB", model.name="TSSEM2 Becker09",
                 mx.algebras=list( Cog=mxAlgebra(Cog2SC*SC2Per, name="Cog"),
                                   SO=mxAlgebra(SO2SC*SC2Per, name="SO"),
                                   Cog_SO=mxAlgebra(Cog2SC*SC2Per+SO2SC*SC2Per,
                                   name="Cog_SO")) )
summary(cluster2)

## Convert the model to semPlotModel object with 2 plots
## Use the short forms of the variable names
my.plots <- lapply(X=cluster2, FUN=meta2semPlot, manNames=c("Per","Cog","SO","SC") )

## Load the library
library("semPlot")

## Setup two plots
layout(t(1:2))
## The labels are overlapped. We may modify it by using layout="spring"
semPaths(my.plots[[1]], whatLabels="est", nCharNodes=10, color="orange",
         layout="spring", edge.label.cex=0.8)
title("Individual sport")
semPaths(my.plots[[2]], whatLabels="est", nCharNodes=10, color="skyblue",
         layout="spring", edge.label.cex=0.8)
title("Team sport")


#### Random-effects model
## First stage analysis
random1 <- tssem1(Becker09$data, Becker09$n, method="REM", RE.type="Diag")
summary(random1)

## Second stage analysis
random2 <- tssem2(random1, Amatrix=A1, Smatrix=S1, diag.constraints=TRUE,
                  intervals.type="LB", model.name="TSSEM2 Becker09",
                  mx.algebras=list( Cog=mxAlgebra(Cog2SC*SC2Per, name="Cog"),
                                    SO=mxAlgebra(SO2SC*SC2Per, name="SO"),
                                    Cog_SO=mxAlgebra(Cog2SC*SC2Per+SO2SC*SC2Per,
                                    name="Cog_SO")) )
summary(random2)

## Load the library
library("semPlot")

## Convert the model to semPlotModel object
my.plot <- meta2semPlot(random2, manNames=c("Per","Cog","SO","SC"))

## Plot the model with labels
## The labels are overlapped.
## semPaths(my.plot, whatLabels="path", nCharEdges=10)
semPaths(my.plot, whatLabels="path", nCharEdges=10,  nCharNodes=10, layout="spring", color="red")

## Plot the parameter estimates
semPaths(my.plot, whatLabels="est", nCharNodes=10, layout="spring", color="green")