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Full-Information Covariance SEM

  • J. Christopher Westland
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 22)

Abstract

Partial least squares path methods elicit only pairwise relationships between latent constructs, though they allude to a more complete “plausible” structure. In the 1950s, the Cowles Commission at the University of Chicago and in the 1970s Karl Jöreskog of Uppsala University extended structural equation model methods to incorporate all of the information in the covariance structure, in what are called full-information methods. These methods placed structural equation models on secure statistical footing, allowing confirmatory testing of models of latent constructs. These models are substantially more complex than the crude methods used in PLS-PA, but provide performance measures that moved structural equation models from exploratory analysis into full-fledged hypothesis testing.

References

  1. Bentler, Peter M. 1995. EQS Structural Equations Program Manual. Vol. 6. Encino, CA: Multivariate Software.Google Scholar
  2. Blau, P.M., O.D. Duncan, and A. Tyree. 1967. “The Process of Stratification.” In Social Stratification. Class, Race & Gender, 317–329. San Francisco: Boulder.Google Scholar
  3. Blunch, N.J. 2008. Introduction to Structural Equation Modelling Using Spss and Amos. Thousand Oaks: Sage Publications Ltd.CrossRefGoogle Scholar
  4. Bollen, Kenneth A. 1989a. Structural Equations with Latent Variables. Vol. 8. New York: Wiley.CrossRefGoogle Scholar
  5. Browne, Michael W., and Robert Cudeck. 1989. “Single Sample Cross-Validation Indices for Covariance Structures.” Multivariate Behavioral Research 24 (4): 445–455.CrossRefGoogle Scholar
  6. ———. 1992. “Alternative Ways of Assessing Model Fit.” Sociological Methods & Research 21 (2): 230–258.CrossRefGoogle Scholar
  7. ———. 1993. “Alternative Ways of Assessing Model Fit.” Sage Focus Editions 154: 136.Google Scholar
  8. Cheung, Gordon W., and Roger B. Rensvold. 2002. “Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance.” Structural Equation Modeling 9 (2): 233–255.MathSciNetCrossRefGoogle Scholar
  9. Cochran, William G., Frederick Mosteller, and John W. Tukey. 1954. “Principles of Sampling.” Journal of the American Statistical Association 49 (265): 13–35.CrossRefGoogle Scholar
  10. Dhrymes, Phoebus J., and H. Erlat. 1972. Asymptotic Properties of Full Information Estimators in Dynamic Autoregressive Simultaneous Models. Report. Los Angeles: UCLA Department of Economics.Google Scholar
  11. Dhrymes, Phoebus J., E. Philip Howrey, Saul H. Hymans, Jan Kmenta, Edward E. Leamer, Richard E. Quandt, James B. Ramsey, Harold T. Shapiro, and Victor Zarnowitz. 1972. “Criteria for Evaluation of Econometric Models. Book Section.” In Annals of Economic and Social Measurement, Volume 1, Number 3, 291–325. Cambridge: NBER.Google Scholar
  12. Fox, John. 2006. Teacher’s Corner: Structural Equation Modeling with the SEM Package in R. Structural Equation Modeling 13 (3): 465–486.MathSciNetCrossRefGoogle Scholar
  13. Harris, R.L. 1999. Information Graphics: A Comprehensive Illustrated Reference. Oxford: Oxford University Press. http://books.google.com/books?id=LT1RXREvkGIC zbMATHGoogle Scholar
  14. Hox, Joop J. 1995. Applied Multilevel Analysis. Amsterdam: TT-publikaties.zbMATHGoogle Scholar
  15. Hu, L., and P.M. Bentler. 1999. “Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives.” Structural Equation Modeling: A Multidisciplinary Journal 6 (1): 1–55.CrossRefGoogle Scholar
  16. Ioannidis, J.P.A. 2005a. “Why Most Published Research Findings Are False.” PLoS Medicine 2 (8): e124.CrossRefGoogle Scholar
  17. Jöreskog, Karl G. 1970. “A General Method for Estimating a Linear Structural Equation System.” ETS Research Bulletin Series 1970 (2): i–41.Google Scholar
  18. Jöreskog, Karl G., and Dag Sörbom. 1996. LISREL 8: User’s Reference Guide. Skokie: Scientific Software International.Google Scholar
  19. Jöreskog, Karl G., and Marielle Van Thillo. 1972. “LISREL: A General Computer Program for Estimating a Linear Structural Equation System Involving Multiple Indicators of Unmeasured Variables.”Google Scholar
  20. Lohmöller, Jan-Bernd. 1988. “The PLS Program System: Latent Variables Path Analysis with Partial Least Squares Estimation.” Multivariate Behavioral Research 23 (1): 125–127.CrossRefGoogle Scholar
  21. ———. 1989. Latent Variable Path Modeling with Partial Least Squares. Heidelberg: Physica-Verlag.CrossRefGoogle Scholar
  22. Lydtin, H., G Lohmöller, R. Lohmöller, H. Schmitz, and I. Walter. 1980. “Hemodynamic Studies on Adalat in Healthy Volunteers and in Patients.” Conference Proceedings. In 2nd International AdalatⓇSymposium, 112–123. Berlin: Springer.Google Scholar
  23. Marsh, H.W., B.M. Byrne, and R. Craven. 1992. “Overcoming Problems in Confirmatory Factor Analyses of Mtmm Data: The Correlated Uniqueness Model and Factorial Invariance.” Multivariate Behavioral Research 27 (4): 489–507.CrossRefGoogle Scholar
  24. McArdle, John J. 1988. “Dynamic but Structural Equation Modeling of Repeated Measures Data.” In Handbook of Multivariate Experimental Psychology, 561–614. Berlin: Springer.CrossRefGoogle Scholar
  25. McArdle, Brian H., and Marti J. Anderson. 2001. “Fitting Multivariate Models to Community Data: A Comment on Distance-Based Redundancy Analysis.” Ecology 82 (1): 290–297.CrossRefGoogle Scholar
  26. McArdle, John J., and David Epstein. 1987. “Latent Growth Curves Within Developmental Structural Equation Models.” Child Development 58 (1): 110–133.CrossRefGoogle Scholar
  27. McIver, John, and Edward G. Carmines. 1981. Unidimensional Scaling. Vol. 24. Thousand Oaks: Sage.CrossRefGoogle Scholar
  28. Raykov, Tenko. 2005. Analysis of Longitudinal Studies with Missing Data Using Covariance Structure Modeling with Full-Information Maximum Likelihood. Structural Equation Modeling 12 (3): 493–505.MathSciNetCrossRefGoogle Scholar
  29. Rosseel, Yves. 2012. Lavaan: An R Package for Structural Equation Modeling and More. Version 0.5–12 (BETA). Journal of Statistical Software 48 (2): 1–36.CrossRefGoogle Scholar
  30. Turner, Malcolm E., and Charles D. Stevens. 1959. “The Regression Analysis of Causal Paths.” Biometrics 15 (2): 236–258.MathSciNetCrossRefGoogle Scholar
  31. Vinzi, Vincenzo Esposito, Carlo N. Lauro, and Silvano Amato. 2005. “PLS Typological Regression: Algorithmic, Classification and Validation Issues.” In New Developments in Classification and Data Analysis, 133–140. Berlin: Springer.CrossRefGoogle Scholar
  32. ———. 1960. “Path Coefficients and Path Regressions: Alternative or Complementary Concepts?” Biometrics 16 (2): 189–202.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Information & Decision SystemsUniversity of Illinois at ChicagoChicagoUSA

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