Cooke16: Correlation Matrices from Cooke et al. (2016)

In mikewlcheung/metasem: Meta-Analysis using Structural Equation Modeling

Correlation Matrices from Cooke et al. (2016)

Description

The data set includes correlation matrices on using the theory of planned behavior to predict alcohol consumption reported by Cooke et al. (2016).

Usage

data(Cooke16)

Details

A list of data with the following structure:

data

A list of correlation matrices. The variables are SN (subjective norm), ATT (attitude), PBC (perceived behavior control), BI (behavioral intention), and BEH (behavior).

n

A vector of sample sizes.

MeanAge

Mean age of the participants except for Ajzen and Sheikh (2013), which is the median age, and Glassman, et al. (2010a) to Glassman, et al. (2010d), which are based on the range of 18 to 24.

Female

Percentage of female participants.

Source

Cooke, R., Dahdah, M., Norman, P., & French, D. P. (2016). How well does the theory of planned behaviour predict alcohol consumption? A systematic review and meta-analysis. Health Psychology Review, 10(2), 148-167.

References

Cheung, M. W.-L., & Hong, R. Y. (2017). Applications of meta-analytic structural equation modeling in health psychology: Examples, issues, and recommendations. Health Psychology Review, 11, 265-279.

Examples

## Check whether the correlation matrices are valid (positive definite)
Cooke16$data[is.pd(Cooke16$data)==FALSE]

## Since the correlation matrix in Study 3 is not positive definite,
## we exclude it in the following analyses
my.data <- Cooke16$data[-3]
my.n <- Cooke16$n[-3]

## Show the no. of studies per correlation
pattern.na(my.data, show.na = FALSE)

## Show the total sample sizes per correlation
pattern.n(my.data, my.n)

## Stage 1 analysis
## Random-effects model
random1 <- tssem1(my.data, my.n, method="REM", RE.type="Diag", acov="weighted")
summary(random1)

A1 <- create.mxMatrix(c(0,0,0,0,0,
                        0,0,0,0,0,
                        0,0,0,0,0,
                        "0.2*SN2BI","0.2*ATT2BI","0.2*PBC2BI",0,0,
                        0,0,"0.2*PBC2BEH","0.2*BI2BEH",0),
                        type="Full", ncol=5, nrow=5,
                        byrow=TRUE, as.mxMatrix=FALSE)

## This step is not necessary but it is useful for inspecting the model.
dimnames(A1)[[1]] <- dimnames(A1)[[2]] <- colnames(Cooke16$data[[1]])

## Display A1
A1

S1 <- create.mxMatrix(c(1,
                        "0.1*ATT_SN", 1,
                        "0.1*PBC_SN", "0.1*PBC_ATT", 1,
                        0, 0, 0, "0.5*VarBI",
                        0, 0, 0, 0, "0.5*VarBEH"),
                      type = "Symm", ncol=5, nrow=5,
                      byrow=TRUE, as.mxMatrix=FALSE)

dimnames(S1)[[1]] <- dimnames(S1)[[2]] <- colnames(Cooke16$data[[1]])
S1

## Stage 2 analysis
random2 <- tssem2(random1, Amatrix=A1, Smatrix=S1, diag.constraints=FALSE,
                  intervals.type="LB")
summary(random2)

## Display the model
plot(random2, what="path")    
    
## Display the model with the parameter estimates
plot(random2, color="yellow")

mikewlcheung/metasem documentation built on April 9, 2024, 2:17 a.m.