2. Basic Concepts and Applications of Structural Equation Models

  1. Xin-Yuan Song and
  2. Sik-Yum Lee

Published Online: 18 JUL 2012

DOI: 10.1002/9781118358887.ch2

Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences

Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences

How to Cite

Song, X.-Y. and Lee, S.-Y. (2012) Basic Concepts and Applications of Structural Equation Models, in Basic and Advanced Bayesian Structural Equation Modeling: With Applications in the Medical and Behavioral Sciences, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9781118358887.ch2

Author Information

  1. Department of Statistics, The Chinese University of Hong Kong

Publication History

  1. Published Online: 18 JUL 2012
  2. Published Print: 24 AUG 2012

Book Series:

  1. Wiley Series in Probability and Statistics

Book Series Editors:

  1. Walter A. Shewhart and
  2. Samuel S. Wilks

ISBN Information

Print ISBN: 9780470669525

Online ISBN: 9781118358887

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Keywords:

  • covariance matrix;
  • fixed covariates;
  • linear SEMs;
  • nonlinear structural equation;
  • structural equation models (SEMs)

Summary

This chapter introduces the basic concepts of structural equation models (SEMs) through models with a linear or a nonlinear structural equation. It illustrates how to apply these models to substantive research, and presents illustrative examples with real medical studies. Linear SEMs are formulated with a measurement equation and a structural equation with linear terms of explanatory latent variables. For the structural equation, fixed covariates give more ingredients to account for the outcome latent variables, in addition to the explanatory latent variables. Nonlinear SEMs are formulated with a measurement equation that is basically the same as in linear SEMs, and a structural equation that is nonlinear in the explanatory latent variables. SEMs provide efficient tools with great flexibility for analyzing multivariate data in behavioral, educational, medical, social, and psychological sciences.

Controlled Vocabulary Terms

covariance matrix; structured questionnaires