uniR2: Second Stage analysis of the univariate R (uniR) approach

Description

It conducts the second stage analysis of the uniR analysis by fitting structural equation models on the average correlation matrix.

Usage

uniR2mx(x, RAM = NULL, Amatrix = NULL, Smatrix = NULL, Fmatrix = NULL,
        model.name=NULL, suppressWarnings=TRUE, silent=TRUE,
        run=TRUE, ...)
uniR2lavaan(x, model, ...)

Value

A fitted object returned from mxRun or sem.

Arguments

x: An object of class uniR1 from uniR1.
RAM: A RAM object including a list of matrices of the model returned from lavaan2RAM.
Amatrix: If RAM is not specified, an Amatrix is required. An asymmetric matrix in the RAM specification with MxMatrix-class. If it is a matrix, it will be converted into MxMatrix-class by the as.mxMatrix function.
Smatrix: If RAM is not specified, an Smatrix is required. A symmetric matrix in the RAM specification with MxMatrix-class. If it is a matrix, it will be converted into MxMatrix-class by the as.mxMatrix function.
Fmatrix: If RAM is not specified, an Fmatrix is required. A filter matrix in the RAM specification with MxMatrix-class. If it is NULL (the default), an identity matrix with the same dimensions of Cov will be created. If it is a matrix, it will be converted into MxMatrix-class by the as.mxMatrix function. It is not required when there is no latent variable.
model.name: A string for the model name in mxModel. If it is missing, the default is "UniR2".
suppressWarnings: Logical. If TRUE, warnings are suppressed. It is passed to mxRun.
silent: Logical. An argument to be passed to mxRun
run: Logical. If FALSE, only return the mx model without running the analysis.
model: A model specified using lavaan syntax see model.syntax
...: Further arguments to be passed to either mxRun or sem. For sem, fixed.x=FALSE is passed automatically.

Author

Mike W.-L. Cheung <mikewlcheung@nus.edu.sg>

Details

This function implements the univariate r approach proposed by Viswesvaran and Ones (1995) to conduct meta-analytic structural equation modeling (MASEM). It treats the average correlation matrix as if it was a covariance matrix in fitting structural equation models. The harmonic mean of the sample sizes in combining correlation coefficients is used as the sample size in fitting structural equation models. It is included in this package for research interests. The two-stage structural equation modeling (TSSEM) approach is preferred (e.g., Cheung, 2015; Cheung & Chan, 2005).

References

Cheung, M. W.-L. (2015). Meta-analysis: A structural equation modeling approach. Chichester, West Sussex: John Wiley & Sons, Inc.

Cheung, M. W.-L., & Chan, W. (2005). Meta-analytic structural equation modeling: A two-stage approach. Psychological Methods, 10, 40-64.

Viswesvaran, C., & Ones, D. S. (1995). Theory testing: Combining psychometric meta-analysis and structural equations modeling. Personnel Psychology, 48, 865-885.