Psychometric Meta-Analysis in R I would like to perform a psychometric meta-analysis of stigma measurement instruments. A potential candidate for a specific analytical approach could be in my opinion the Hunter and Schmidt method: http://www.metafor-project.org/doku.php/tips:hunter_schmidt_method. This method aims to explain a proportion of artifact or measurement error in an instrument (i.e., test measuring attitudes, job performance, etc.). You can find an example here: http://onlinelibrary.wiley.com/doi/10.1111/j.1744-6570.1991.tb00688.x/full (older article in Personnel Psychology Journal). These methods are sometimes also called validity (or reliability) generalization procedures.
I would like to apply this particular method in R and what I was hoping to find was an analytical example of how to apply the procedure in R. I've found that metafor package seems to include some of the Hunter-Schmidt formulas; however, does not offer any examples (such as here): http://www.metafor-project.org/doku.php/analyses. Psychometric meta-analyses seem to be not as common as for example meta-analyses of Randomised Control Trials. 
I would be grateful if anyone would be able to refer me to a good example of how was the procedure mentioned above conducted, ideally in R. Or offer some hints, tips to start such a meta-analysis.
Edit: Based on suggestions from users, here's a brief example of how data may look like and what would be the aim or objective of meta-analysis. At this stage, I am unable to provide a real-world example, therefore the snippet below shows data set through generic example. These examples assume that all scales measure the same or similar construct; however, they might differ in the content and score range.
Example with same validity evidence and reliability measures:  
Name of Scale       Validity evidence              Reliability measure
Scale A             Correlation coefficient        Cronbach's Alpha
Scale B             Correlation coefficient        Cronbach's Alpha
Scale C             Correlation coefficient        Cronbach's Alpha

Example with different validity evidence and reliability measures:  
Name of Scale       Validity evidence              Reliability measure
Scale A             Correlation coefficient        Cronbach's Alpha
Scale B             Confirmatory Factor Analysis   Omega coefficient
Scale C             Content validity               Cronbach's Alpha

The meta-analytical aim would be to generalize validity evidence of Scale A to C and say to what extent these instruments provide a) sufficient validity evidence; similarly, regarding reliability, it would be to say to what extent are these instruments b) precise on a general level. Ideally, I would be able to take into account c) sociodemographic characteristics of samples these instruments used and explain a proportion of artifact or measurement error across instruments. I am not an experienced user regarding psychometric meta-analysis so this example may not necessarily be sufficient, apologies in advance.
 A: The psychmeta package (http://cran.r-project.org/package=psychmeta) has implemented a wide array of psychometric meta-analysis models.
The ma_r() function implements a variety of psychometric meta-analysis models for correcting for sampling error, measurement error, range restriction, and other artifacts. 
For an overview of the package, run vignette("overview", package = "psychmeta") in R. 
A: Hunter and Schmidt (1990) and 2004 as well as Hunter, Schmidt and Jackson (1982) and othes eg Borenstein have postulated several meta-analytic approaches - psychometric approaches. The choice of a technique depends on the goal of your study (eg. Validity generalization)  and the data ( here effect sizes, reliability coefficients, sample sizes of studies design of study etc.) at hand. You seem to be interested in validity generalization of stigma scale ! Look for an appriate "psychometric" meta analysis  method like that of Hunter and Schmidt (2004) or some other methods. It is not too important to execute formulas in R specifically.
