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

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  • Soyeon Ahn
  • John M. Abbamonte

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 (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.

Suggested Citation

  • Soyeon Ahn & John M. Abbamonte, 2020. "A new approach for handling missing correlation values for meta‐analytic structural equation modeling: Corboundary R package," Campbell Systematic Reviews, John Wiley & Sons, vol. 16(1), March.
    • Handle: RePEc:wly:camsys:v:16:y:2020:i:1:n:e1068
    DOI: 10.1002/cl2.1068
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    1. Lewandowski, Daniel & Kurowicka, Dorota & Joe, Harry, 2009. "Generating random correlation matrices based on vines and extended onion method," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 1989-2001, October.
    2. Joe, Harry, 2006. "Generating random correlation matrices based on partial correlations," Journal of Multivariate Analysis, Elsevier, vol. 97(10), pages 2177-2189, November.
    3. Kawee Numpacharoen & Amporn Atsawarungruangkit, 2012. "Generating Correlation Matrices Based on the Boundaries of Their Coefficients," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-7, November.
    4. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
    5. Kawee Numpacharoen & Kornkanok Bunwong, 2013. "Boundaries of Correlation Adjustment with Applications to Financial Risk Management," Applied Mathematical Finance, Taylor & Francis Journals, vol. 20(4), pages 403-414, September.
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