Skip to main content

A Brief History of Structural Equation Models

  • Chapter

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 22))

Abstract

Though structural equation models today are usually associated with soft problems in the social sciences, they had their origin in the natural sciences—specifically biology. Europe’s nineteenth-century scholars were challenged to make sense of the diverse morphologies observed during an age of explorations, in Asia, Africa, and the Americas, as well as at home. In this period, new species of plants and animals were transplanted, domesticated, eaten, and bred at an unprecedented rate. An American ultimately provided one statistical tool that allowed scholars to build a science out of their diverse observations.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Anderson, C. A. (1983). The causal structure of situations: the generation of plausible causal attributions as a function of type of event situation. Journal of Experimental Social Psychology, 19(2), 185–203.

    Article  Google Scholar 

  • Anderson, T. W. (2005). Origins of the limited information maximum likelihood and two-stage least squares estimators. Journal of Econometrics, 127(1), 1–16.

    Article  MathSciNet  Google Scholar 

  • Anderson, T. W., Kunitomo, N., & Matsushita, Y. (2010). On the asymptotic optimality of the LIML estimator with possibly many instruments. Journal of Econometrics, 157(2), 191–204.

    Article  MathSciNet  Google Scholar 

  • Anderson, T. W., & Rubin, H. (1949). Estimation of the parameters of a single equation in a complete system of stochastic equations. Annals of Mathematical Statistics, 20(1), 46–63.

    Article  MATH  MathSciNet  Google Scholar 

  • Anderson, T. W., & Rubin, H. (1950). The asymptotic properties of estimates of the parameters of a single equation in a complete system of stochastic equations. Annals of Mathematical Statistics, 21, 570–582.

    Article  MATH  MathSciNet  Google Scholar 

  • Barabási, A.‐L., Dezső, Z., Ravasz, E., Yook, S.‐H., & Oltvai, Z. (2003). Scale-free and hierarchical structures in complex networks. Paper presented at the Modeling of Complex Systems: Seventh Granada Lectures.

    Google Scholar 

  • Barabási, A. L., & Oltvai, Z. N. (2004). Network biology: understanding the cell’s functional organization. Nature Reviews Genetics, 5(2), 101–113.

    Article  Google Scholar 

  • Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares (PLS) approach to causal modeling: personal computer adoption and use as an illustration. Technology Studies, 2(2), 285–309.

    Google Scholar 

  • Basmann, R.L. (1988). Causality tests and observationally equivalent representations of econometric models. Journal of Econometrics 39(1), 69–104.

    Google Scholar 

  • Browne, M. W., & Cudeck, R. (1989). Single sample cross-validation indices for covariance structures. Multivariate Behavioral Research, 24(4), 445–455.

    Article  Google Scholar 

  • Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2), 230–258.

    Article  Google Scholar 

  • Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit (Sage focus editions, Vol. 154, p. 136). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Chin, W. W. (1998). Commentary: issues and opinion on structural equation modeling. MIS Quarterly, 22, vii.

    Google Scholar 

  • Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. In Statistical strategies for small sample research (Vol. 2, pp. 307–342). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Christ, C. F. (1994). The Cowles Commission’s contributions to econometrics at Chicago, 1939–1955. Journal of Economic Literature, 32, 30–59.

    Google Scholar 

  • Cowles, A. (1933). Can stock market forecasters forecast? Econometrica, 1, 309–324.

    Article  Google Scholar 

  • Cowles, A., 3rd, & Chapman, E. N. (1935). A statistical study of climate in relation to pulmonary tuberculosis. Journal of the American Statistical Association, 30(191), 517–536.

    Article  Google Scholar 

  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35, 982–1003.

    Article  Google Scholar 

  • Dhrymes, P. J. (1971a). Distributed lags. San Francisco, CA: Holden-Day Inc.

    MATH  Google Scholar 

  • Dhrymes, P. J. (1971b). Equivalence of iterative Aitken and maximum likelihood estimators for a system of regression equations. Australian Economic Papers, 10(16), 20–24.

    Article  Google Scholar 

  • Dhrymes, P. J., Berner, R., & Cummins, D. (1974). A comparison of some limited information estimators for dynamic simultaneous equations models with autocorrelated errors. Econometrica, 42, 311–332.

    Article  MATH  MathSciNet  Google Scholar 

  • Farebrother, R. W. (1999). Fitting linear relationships: a history of the calculus of observations 1750–1900. New York, NY: Springer.

    Book  MATH  Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.

    Article  Google Scholar 

  • Freedman, D. A. (1987). As others see us: a case study in path analysis. Journal of Educational and Behavioral Statistics, 12(2), 101–128.

    Google Scholar 

  • Gauss, K. F. (1809). Teoria motus corporum coelestium in sectionibus conicus solem ambientieum.

    Google Scholar 

  • Goldberger, A. S., & Hauser, R. (1971). The treatment of unobservable variables in path analysis. Sociological Methodology, 3(8), 1.

    Google Scholar 

  • Hood, W. C., Koopmans, T. C., & Cowles Commission for Research in Economics. (1953). Studies in econometric method (Vol. 14). New York, NY: Wiley.

    MATH  Google Scholar 

  • Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28(3/4), 321–377.

    Article  MATH  Google Scholar 

  • Jöreskog, K. G. (1967). Some contributions to maximum likelihood factor analysis. Psychometrika, 32(4), 443–482.

    Article  MATH  MathSciNet  Google Scholar 

  • Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.

    Article  Google Scholar 

  • Jöreskog, K. G. (1970). A general method for analysis of covariance structures. Biometrika, 57(2), 239–251.

    Article  MATH  MathSciNet  Google Scholar 

  • Jöreskog, K. G., & Van Thillo, M. (1972). LISREL: a general computer program for estimating a linear structural equation system involving multiple indicators of unmeasured variables. ETS Research Bulletin Series, 1972, i–72.

    Article  Google Scholar 

  • Legendre, A. M. (1977). Note par M.*** Second supplement to the third edition of Legendre (1805). A separate pagination. English translation by Stigler. pp. 79–80.

    Google Scholar 

  • Lohmöller, J. B. (1981). Pfadmodelle mit latenten variablen: LVPLSC ist eine leistungsfähige alternative zu LIDREL. München: Hochsch. d. Bundeswehr, Fachbereich Pädagogik.

    Google Scholar 

  • Milgram, S. (1967). The small world problem. Psychology Today, 2(1), 60–67.

    MathSciNet  Google Scholar 

  • Quetelet, A. (1835). Sur l’homme et le développement de ses facultés ou essai de physique sociale. Bachelier, Paris.

    Google Scholar 

  • Sargan, J. D. (1958). The estimation of economic relationships using instrumental variables. Econometrica, pp. 393–415.

    Google Scholar 

  • Spearman, Charles, and Ll Wynn Jones. “Human ability.” (1950).

    Google Scholar 

  • Stigler, S. M. (1981). Gauss and the invention of least squares. The Annals of Statistics, pp. 465–474.

    Google Scholar 

  • Theil, H. (1953). Repeated least squares applied to complete equation systems. Central Planning Bureau, The Hague.

    Google Scholar 

  • Theil, H. (1992). Estimation and simultaneous correlation in complete equation systems. Henri Theil’s contributions to economics and econometrics, pp. 65–107. Springer, Netherlands.

    Book  Google Scholar 

  • Theil, H. (1961). Economic forecasts and policy.

    Google Scholar 

  • Tukey, J. W. (1954). Causation, regression, and path analysis. Statistics and Mathematics in Biology: 35–66.

    Google Scholar 

  • Turner, M. E., & Stevens, C. D. (1959). The regression analysis of causal paths. Biometrics, 15(2), 236–258.

    Article  MATH  MathSciNet  Google Scholar 

  • Werts, C. E., & Linn, R. L. (1970). Path analysis: psychological examples. Psychological Bulletin, 74(3), 193.

    Article  Google Scholar 

  • Werts, C. E., Linn, R. L., & Jöreskog, K. G. (1974). Intraclass reliability estimates: testing structural assumptions. Educational and Psychological Measurement, 34(1), 25–33.

    Article  Google Scholar 

  • Westland, J. C. (2010). Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications, 9(6), 476–487.

    Article  Google Scholar 

  • Wold, H. (1966). Estimation of principal components and related models by iterative least squares. Multivariate Analysis, 1, 391–420.

    MathSciNet  Google Scholar 

  • Wold, H. (1973). Nonlinear iterative partial least squares (NIPALS) modelling: some current developments. Multivariate Analysis, 3, 383–407.

    Google Scholar 

  • Wold, H. (1974). Causal flows with latent variables: partings of the ways in the light of NIPALS modelling. European Economic Review, 5(1), 67–86.

    Article  Google Scholar 

  • Wold, H. (1975). Path models with latent variables: the NIPALS approach. New York, NY: Academic Press.

    Google Scholar 

  • Wright, S. (1960). Path coefficients and path regressions: alternative or complementary concepts? Biometrics 16(2), 189–202.

    Article  MATH  Google Scholar 

  • Wright, S. (1920). The relative importance of heredity and environment in determining the piebald pattern of guinea-pigs. Proceedings of the National Academy of Sciences of the United States of America, 6(6), 320.

    Article  Google Scholar 

  • Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557–585.

    Google Scholar 

  • Wright, S. (1934). The method of path coefficients. Annals of Mathematical Statistics, 5(3), 161–215.

    Article  Google Scholar 

  • Wright, S. (1960). Path coefficients and path regressions: alternative or complentary concepts?. Biometrics 16(2), 189–202.

    Google Scholar 

  • Zellner, A. (1962). An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association, 57, 348–368.

    Article  MATH  MathSciNet  Google Scholar 

  • Zellner, A., & Theil, H. (1962). Three-stage least squares: simultaneous estimation of simultaneous equations. Econometrica, 30, 54–78.

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Westland, J.C. (2015). A Brief History of Structural Equation Models. In: Structural Equation Models. Studies in Systems, Decision and Control, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-16507-3_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-16507-3_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16506-6

  • Online ISBN: 978-3-319-16507-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics