Advertisement

Canonical Correlation Analysis with Missing Values: A Structural Equation Modeling Approach

  • Zhenqiu (Laura) LuEmail author
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 265)

Abstract

Canonical correlation analysis (CCA) is a generalization of multiple correlation that examines the relationship between two sets of variables. When there are missing values, spectral decomposition in CCA becomes complicated and difficult to implement. This article investigates structural equation modeling approach to Canonical correlation analysis when data have missing values.

Keywords

Canonical correlation analysis Structural equation modeling Missing values 

References

  1. Anderson, T. W. (2003). An introduction to multivariate statistical analysis (3rd ed.). New York, NY: Wiley.zbMATHGoogle Scholar
  2. Bagozzi, R. P., Fomell, C., & Larcker, D. F. (1981). Canonical correlation analysis as a special case of a structural relations model. Multivariate Behavioral Research, 16, 437–454.CrossRefGoogle Scholar
  3. Bentler, P. M. (1995). EQS structural equations program manual. Multivariate Software.Google Scholar
  4. Boker, S., Neale, M., Maes, H., Wilde, M., Spiegel, M., Brick, T., et al. (2011). OpenMx: An open source extended structural equation modeling framework. Psychometrika, 76(2), 306–317.MathSciNetCrossRefGoogle Scholar
  5. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.CrossRefGoogle Scholar
  6. Enders, C. K., & Bandalos, D. L. (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling, 8(3), 430–457.MathSciNetCrossRefGoogle Scholar
  7. Fan, X. (1997). Canonical correlation analysis and structural equation modeling: What do they have in common? Structural Equation Modeling, 4(1), 65–79.MathSciNetCrossRefGoogle Scholar
  8. Food and Agriculture Organization of the United Nations. (1998). FAOSTAT statistics database. http://www.fao.org/faostat/en/#data.
  9. Fox, J. (2006). Teacher’s corner: Structural equation modeling with the sem package in R. Structural Equation Modeling, 13(3), 465–486.MathSciNetCrossRefGoogle Scholar
  10. Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28, 321–377.CrossRefGoogle Scholar
  11. Jöreskog, K. G., & Sörbom, D. (2006). LISREL 8.80 for windows [Computer software]. Lincolnwood, IL: Scientific Software International.Google Scholar
  12. Lu, Z. L., & Gu, F. (2018). A structural equation modeling approach to canonical correlation analysis. Quantitative psychology (pp. 261–273). Cham: Springer.Google Scholar
  13. MathWorks, Inc. (2012). MATLAB and statistics toolbox. Massachusetts: Natick.Google Scholar
  14. Muthén, B. O., & Muthén, L. K. (2012). Software Mplus version 7.Google Scholar
  15. R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/.
  16. Rosseel, Y., Oberski, D., Byrnes, J., Vanbrabant, L., & Savalei, V. (2013). Lavaan: Latent variable analysis [Software]. http://CRAN.R-project.org/package=lavaan (R package version 0.5-14).
  17. SAS Institute Inc. (1993). SAS/STAT software.Google Scholar
  18. SPSS, I. (2012). Statistics for windows, version 20.0. IBM Corporation, Armonk, NY.Google Scholar
  19. Yuan, K.-H., & Lu, L. (2008). SEM with missing data and unknown population distributions using two-stage ML: Theory and its application. Multivariate Behavioral Research, 43(4), 621–652.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of GeorgiaAthensUSA

Personalised recommendations