metaSEM (version 0.9.8)

Becker92: Six Studies of Correlation Matrices reported by Becker (1992; 1995)

Description

This data set includes six studies of correlation matrices reported by Becker (1992; 1995).

Usage

data(Becker92)

Source

Becker, B. J. (1992). Using results from replicated studies to estimate linear models. Journal of Educational Statistics, 17(4), 341-362. doi:10.3102/10769986017004341 Becker, B. J. (1995). Corrections to "Using Results from Replicated Studies to Estimate Linear Models." Journal of Educational and Behavioral Statistics, 20(1), 100-102. doi:10.2307/1165390

Details

A list of data with the following structure:

data

A list of 6 studies of correlation matrices. The variables are Math aptitude, Spatial ability and Verbal ability

n

A vector of sample sizes

Examples

## Not run: 
# data(Becker92)
# 
# #### Fixed-effects model
# ## First stage analysis
# ## Replicate Becker's (1992) analysis using 4 studies only
# fixed1 <- tssem1(Becker92$data[1:4], Becker92$n[1:4], method="FEM")
# summary(fixed1)
# 
# ## Prepare a regression model using create.mxMatrix()
# A1 <- create.mxMatrix(c(0,0,0,"0.2*Spatial2Math",
#                         0,0,"0.2*Verbal2Math",0,0), type="Full",
#                         ncol=3, nrow=3, as.mxMatrix=FALSE)
# 
# ## This step is not necessary but it is useful for inspecting the model.
# dimnames(A1)[[1]] <- dimnames(A1)[[2]] <- c("Math","Spatial","Verbal") 
# 
# ## Display A1
# A1
# 
# S1 <- create.mxMatrix(c("0.2*ErrorVarMath",0,0,1,"0.2*CorSpatialVerbal",1),
#                         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("Math","Spatial","Verbal") 
# 
# ## Display S1
# S1
# 
# ################################################################################
# ## Alternative model specification in lavaan model syntax
# model <- "## Regression paths
#           Math~Spatial2Math*Spatial
#           Math~Verbal2Math*Verbal
#           Spatial~~CorSpatialVerbal*Verbal
#           ## Fix the variances of Spatial and Verbal at 1
#           Spatial~~1*Spatial
#           Verbal~~1*Verbal
#           ## Label the error variance of Math
#           Math~~ErrorVarMath*Math+start(0.2)*Math"
# 
# RAM <- lavaan2RAM(model, obs.variables=c("Math","Spatial","Verbal"))
# RAM
# 
# A1 <- RAM$A
# S1 <- RAM$S
# ################################################################################
# 
# ## Fixed-effects model: Second stage analysis
# ## Two equivalent versions to calculate the R2 and its 95% LBCI
# fixed2 <- tssem2(fixed1, Amatrix=A1, Smatrix=S1, intervals.type="LB",
#        mx.algebras=list(R1=mxAlgebra(Spatial2Math^2+Verbal2Math^2
#                         +2*CorSpatialVerbal*Spatial2Math*Verbal2Math, name="R1"),
#                         R2=mxAlgebra(One-Smatrix[1,1], name="R2"),
#                         One=mxMatrix("Iden", ncol=1, nrow=1, name="One")))
# summary(fixed2)
# 
# #### Random-effects model
# ## First stage analysis
# ## No random effects for off-diagonal elements
# random1 <- tssem1(Becker92$data, Becker92$n, method="REM", RE.type="Diag")
# summary(random1)
# 
# ## Random-effects model: Second stage analysis
# random2 <- tssem2(random1, Amatrix=A1, Smatrix=S1, intervals.type="z")
# summary(random2)
# 
# #### Conventional fixed-effects GLS approach
# ## First stage analysis
# ## No random effects
# ## Replicate Becker's (1992) analysis using 4 studies only
# gls1 <- tssem1(Becker92$data[1:4], Becker92$n[1:4], method="REM", RE.type="Zero",
#                model.name="Fixed effects GLS Stage 1")
# summary(gls1)
# 
# ## Fixed-effects GLS model: Second stage analysis
# gls2 <- tssem2(gls1, Amatrix=A1, Smatrix=S1, intervals.type="z",
#                model.name="Fixed effects GLS Stage 2")
# summary(gls2)
# 
# ## Load the library
# library("semPlot")
# 
# ## Convert the model to semPlotModel object
# my.plot <- meta2semPlot(gls2)
# 
# ## Plot the model with labels
# semPaths(my.plot, whatLabels="path", nCharEdges=10, nCharNodes=10, color="red")
# 
# ## Plot the parameter estimates
# semPaths(my.plot, whatLabels="est", nCharNodes=10, color="green")
# ## End(Not run)