It conducts the first stage analysis of the uniR analysis by pooling elements of the correlation coefficients individually.
uniR1(Cor, n, ...)An object of class uniR1 of the original data, the sample
sizes, the harmonic mean of sample sizes, the average correlation
matrix, the standard errors of the correlation matrix, and the
standard deviations (heterogeneity) of the correlation matrix.
A list of correlation matrices
A vector of sample sizes
Further arguments which are currently ignored
Mike W.-L. Cheung <mikewlcheung@nus.edu.sg>
This function implements the univariate r approach proposed by Viswesvaran and Ones (1995) to conduct meta-analytic structural equation modeling (MASEM). It uses Schmidt and Hunter's approach to combine correlation coefficients. 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).
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.
Schmidt, F. L., & Hunter, J. E. (2015). Methods of meta-analysis: Correcting error and bias in research findings (3rd ed.). Thousand Oaks, CA: Sage.
Viswesvaran, C., & Ones, D. S. (1995). Theory testing: Combining psychometric meta-analysis and structural equations modeling. Personnel Psychology, 48, 865-885.
uniR2, Becker09