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

uniR2 {metaSEM}

R Documentation

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, ...)

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.

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).

Value

A fitted object returned from mxRun or sem.

Author(s)

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

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.