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 (rs) in the correlation matrix of m variables (R( m × m )) and establish more informative and empirical prior distributions for missing rs in R( m × m ). In particular, the method for deriving mathematically/analytically boundaries for missing rs in relation to other adjacent rs 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 rs in R( m × m ) is demonstrated with an example. Furthermore, the applicability of constructing empirical boundaries for rs in R( m × m ) beyond multivariate meta‐analysis is discussed.