metaSEM (version 0.9.8)

Hunter83: Fourteen Studies of Correlation Matrices reported by Hunter (1983)

Description

This data set includes fourteen studies of Correlation Matrices reported by Hunter (1983)

Usage

data(Hunter83)

Source

Hunter, J. E. (1983). A causal analysis of cognitive ability, job knowledge, job performance, and supervisor ratings. In F. Landy, S. Zedeck, & J. Cleveland (Eds.), Performance Measurement and Theory (pp. 257-266). Hillsdale, NJ: Erlbaum.

Details

A list of data with the following structure:

data

A list of 14 studies of correlation matrices. The variables are Ability, Job knowledge, Work sample and Supervisor rating

n

A vector of sample sizes

Examples

## Not run: 
# data(Hunter83)
# 
# #### Fixed-effects model
# ## First stage analysis
# fixed1 <- tssem1(Hunter83$data, Hunter83$n, method="FEM",
#                  model.name="TSSEM1 fixed effects model")
# summary(fixed1)
# 
# #### Second stage analysis
# ## Model without direct effect from Ability to Supervisor
# A1 <- create.mxMatrix(c(0,"0.1*A2J","0.1*A2W",0,0,0,"0.1*J2W","0.1*J2S",
#                         0,0,0,"0.1*W2S",0,0,0,0),
#                         type="Full", 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("Ability","Job","Work","Supervisor") 
# A1
# 
# S1 <- create.mxMatrix(c(1,"0.1*Var_e_J", "0.1*Var_e_W", "0.1*Var_e_S"),
#                       type="Diag", as.mxMatrix=FALSE)
# dimnames(S1)[[1]] <- dimnames(S1)[[2]] <- c("Ability","Job","Work","Supervisor") 
# S1
# 
# ################################################################################
# ## Alternative model specification in lavaan model syntax
# model <- "## Regression paths
#           Job~A2J*Ability
#           Work~A2W*Ability + J2W*Job
#           Supervisor~J2S*Job + W2S*Work
#           ## Fix the variance of Ability
#           Ability~~1*Ability
#           ## Label the error variances of dependent variables
#           Job~~Var_e_J*Job
#           Work~~Var_e_W*Work
#           Supervisor~~Var_e_S*Supervisor"
# 
# RAM <- lavaan2RAM(model, obs.variables=c("Ability","Job","Work","Supervisor"))
# RAM
# 
# A1 <- RAM$A
# S1 <- RAM$S
# ################################################################################
# 
# ## diag.constraints=TRUE is required as there are mediators  
# fixed2 <- tssem2(fixed1, Amatrix=A1, Smatrix=S1, intervals.type="LB",
#                  diag.constraints=FALSE,
#                  model.name="TSSEM2 fixed effects model")
# summary(fixed2)
# 
# ## Coefficients
# coef(fixed2)
# 
# ## VCOV based on parametric bootstrap
# vcov(fixed2)
# 
# #### Random-effects model with diagonal elements only
# ## First stage analysis
# random1 <- tssem1(Hunter83$data, Hunter83$n, method="REM", RE.type="Diag", 
#                   model.name="TSSEM1 random effects model")
# summary(random1)
# 
# ## Second stage analysis
# ## Model without direct effect from Ability to Supervisor
# 
# ## diag.constraints=TRUE is required as there are mediators 
# random2 <- tssem2(random1, Amatrix=A1, Smatrix=S1, intervals.type="LB",
#                   diag.constraints=FALSE,
#                   mx.algebras=
#                   list( ind=mxAlgebra(A2J*J2S+A2J*J2W*W2S+A2W*W2S, name="ind") ),
#                   model.name="TSSEM2 random effects model")
# summary(random2)
# 
# ## Load the library
# library("semPlot")
# 
# ## Convert the model to semPlotModel object
# my.plot <- meta2semPlot(random2)
# 
# ## 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)