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Using Path Diagrams as a Structural Equation Modeling Tool

Author

Listed:
  • PETER SPIRTES

    (Carnegie Mellon University)

  • THOMAS RICHARDSON

    (University of Washington)

  • CHRISTOPHER MEEK

    (Microsoft Corporation)

  • RICHARD SCHEINES

    (Carnegie Mellon University)

  • CLARK GLYMOUR

    (Carnegie Mellon University and the University of California, San Diego)

Abstract

A linear structural equation model (SEM) without free parameters has two parts: a probability distribution and an associated path diagram corresponding to the causal relations among variables specified by the structural equations and the correlations among the error terms. This article shows how path diagrams can be used to solve a number of important problems in structural equation modeling; for example, How much do sample data underdetermine the correct model specification? Given that there are equivalent models, is it possible to extract the features common to those models? When a modeler draws conclusions about coefficients in an unknown underlying SEM from a multivariate regression, precisely what assumptions are being made about the SEM? The authors explain how the path diagram provides much more than heuristics for special cases; the theory of path diagrams helps to clarify several of the issues just noted.

Suggested Citation

  • Peter Spirtes & Thomas Richardson & Christopher Meek & Richard Scheines & Clark Glymour, 1998. "Using Path Diagrams as a Structural Equation Modeling Tool," Sociological Methods & Research, , vol. 27(2), pages 182-225, November.
    • Handle: RePEc:sae:somere:v:27:y:1998:i:2:p:182-225
    DOI: 10.1177/0049124198027002003
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    References listed on IDEAS

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    1. Klepper Steven & Kamlet Mark S. & Frank Richard G., 1993. "Regressor Diagnostics for the Errors-in-Variables Model - An Application to the Health Effects of Pollution," Journal of Environmental Economics and Management, Elsevier, vol. 24(3), pages 190-211, May.
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    Cited by:

    1. Dirk Temme, 2006. "Constraint-based inference algorithms for structural models with latent confounders— empirical application and simulations," Computational Statistics, Springer, vol. 21(1), pages 151-182, March.
    2. Roderick McDonald, 2002. "What can we learn from the path equations?: Identifiability, constraints, equivalence," Psychometrika, Springer;The Psychometric Society, vol. 67(2), pages 225-249, June.
    3. Steffen L. Lauritzen & Thomas S. Richardson, 2002. "Chain graph models and their causal interpretations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 321-348, August.
    4. Marchetti, Giovanni M. & Stanghellini, Elena, 2008. "A note on distortions induced by truncation with applications to linear regression systems," Statistics & Probability Letters, Elsevier, vol. 78(6), pages 824-829, April.
    5. Jürgen Muehlbacher & Michaela Nettekoven & Jure Kovac, 2011. "Coping with the market: Are there cohort effects for organisations in transition?," Journal of East European Management Studies, Nomos Verlagsgesellschaft mbH & Co. KG, vol. 16(1), pages 75-90.

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