metaSEM (version 0.9.8)

Roorda11: Studies on Students' School Engagement and Achievement Reported by Roorda et al. (2011)

Description

The data set includes 45 studies on the influence of affective teacher-student relationships on students' school engagement and achievement reported by Roorda et al. (2011).

Usage

data(Roorda11)

Source

Roorda, D. L., Koomen, H. M. Y., Spilt, J. L., & Oort, F. J. (2011). The influence of affective teacher-student relationships on students' school engagement and achievement a meta-analytic approach. Review of Educational Research, 81(4), 493-529.

Details

The variables are:

data

A list of 45 studies of correlation matrices. The variables are pos (positive teacher-student relations), neg (negative teacher-student relations), enga (student engagement), and achiev (student achievement).

n

A vector of sample sizes

SES

A vector of average socio-economic status (SES) of the samples

References

Jak, S., & Cheung, M. W.-L. (2015). Addressing heterogeneity in meta-analytic structural equation modeling using subgroup analysis. Manuscript in preparation.

Examples

## Not run: 
# 
# ## Random-effects model: First stage analysis
# random1 <- tssem1(my.df = Roorda11$data, n = Roorda11$n, method = "REM", RE.type = "Diag")
# summary(random1)
# 
# varnames <- c("pos", "neg", "enga", "achiev")
# 
# ## Prepare a regression model using create.mxMatrix()
# A <- create.mxMatrix(c(0,0,0,0,
#                        0,0,0,0,
#                        "0.1*b31","0.1*b32",0,0,
#                        0,0,"0.1*b43",0),
#                      type = "Full", nrow = 4, ncol = 4, byrow = TRUE,
#                      name = "A", as.mxMatrix = FALSE)
# 
# ## This step is not necessary but it is useful for inspecting the model.
# dimnames(A) <- list(varnames, varnames)
# A
# 
# S <- create.mxMatrix(c(1,
#                        ".5*p21",1,
#                        0,0,"0.6*p33",
#                        0,0,0,"0.6*p44"), 
#                      type="Symm", byrow = TRUE,
#                      name="S", as.mxMatrix = FALSE)
# 
# ## This step is not necessary but it is useful for inspecting the model.
# dimnames(S) <- list(varnames, varnames)
# S
# 
# ## Random-effects model: Second stage analysis
# random2 <- tssem2(random1, Amatrix=A, Smatrix=S, diag.constraints=TRUE, 
#                   intervals="LB")
# summary(random2)
# 
# ## Load the library
# library("semPlot")
# 
# ## Convert the model to semPlotModel object
# my.plot <- meta2semPlot(random2)
# 
# ## Plot the parameter estimates
# semPaths(my.plot, whatLabels="est", nCharNodes=10, color="green")
# ## End(Not run)