# 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.

• Do you just need to meta-analyze psychometric data, or do you need some particular method or family of methods called "psychometric meta-analysis"? If the former, why not use generic meta-analytic methods? Aug 18 '17 at 23:59
• @Kodiologist If by psychometric data you mean evidence of reliability such as Cronbach's alpha and validity such as correlation - then yes, it's psychometric data. However; when I did my research on this topic, I've found good results only when focusing on "psychometric meta-analysis" such as the one described by Hunter-Schmidt. So I came to the conclusion that to analyse such data I need to approach it in a similar direction. Either way, I still don't know how would I approach such data with either "generic" or "psychometric" meta-analysis, hence the question. Aug 21 '17 at 11:07
• What do you mean by RCT ? And what is its expansion ? Sep 7 '17 at 16:51
• The question appears to be good . BUT, I still find that it is vague. Sep 7 '17 at 16:55
• @subhashc.davar I've edited the question to make it more clear. Hope it helps a bit. Sep 8 '17 at 11:41