A new approach for handling missing correlation values for meta‐analytic structural equation modeling: Corboundary R package. Issue 1 (31st January 2020)

• Record Type: Journal Article
• Authors: Ahn, Soyeon; Abbamonte, John M.
• Is Part Of: Campbell systematic reviews. Volume 16:Issue 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-01-31
• Subjects: boundary -- meta‐analysis -- missing correlation; Criminology -- Periodicals; Education -- Periodicals; Public welfare -- Periodicals; Criminology; Education; Public welfare; Periodicals; Electronic journals; 362
• DOI: 10.1002/cl2.1068
• Languages: English
• ISSNs: 1891-1803

Abstract

Abstract: With increased use of multivariate meta‐analysis in numerous disciplines, where structural relationships among multiple variables are examined, researchers often encounter a particular challenge due to missing information. The current research concerns missing correlations ( r s) in the correlation matrix of m variables (R m × m ) and establish more informative and empirical prior distributions for missing r s in R m × m . In particular, the method for deriving mathematically/analytically boundaries for missing r s in relation to other adjacent r s in R m × m, while satisfying conditions for a valid R m × m (i.e., a symmetric and positive semidefinite correlation matrix containing real numbers between −1 and 1) is first discussed. Then, a user‐defined R package for constructing the empirical distributions of boundaries for r s in R m × m is demonstrated with an example. Furthermore, the applicability of constructing empirical boundaries for r s in R m × m beyond multivariate meta‐analysis is discussed.