Roorda11: Studies on Students' School Engagement and Achievement...

In metaSEM: Meta-Analysis using Structural Equation Modeling

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)

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

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.

References

Jak, S., & Cheung, M. W.-L. (2018). Addressing heterogeneity in meta-analytic structural equation modeling using subgroup analysis. Behavior Research Methods, 50, 1359-1373.

Examples

## Random-effects model: First stage analysis
random1 <- tssem1(Cov = 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)

## Display the model with the parameter estimates    
plot(random2)

metaSEM documentation built on Sept. 30, 2024, 9:21 a.m.