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

Soyeon Ahn, John M. Abbamonte

Research output: Contribution to journalArticlepeer-review


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 (Rm × m) and establish more informative and empirical prior distributions for missing rs in Rm × m. In particular, the method for deriving mathematically/analytically boundaries for missing rs in relation to other adjacent rs in Rm × m, while satisfying conditions for a valid Rm × 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 Rm × m is demonstrated with an example. Furthermore, the applicability of constructing empirical boundaries for rs in Rm × m beyond multivariate meta-analysis is discussed.

Original languageEnglish (US)
Article numbere1068
JournalCampbell Systematic Reviews
Issue number1
StatePublished - Jan 1 2020


  • boundary
  • meta-analysis
  • missing correlation

ASJC Scopus subject areas

  • Social Sciences(all)

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