A new approach for handling missing correlation values for meta‐analytic structural equation modeling: Corboundary R package

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.