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Fitting structural equation models using estimating equations: a model segregation approach.

Ke-Hai Yuan1, Wai Chan

  • 1Dept of Psychology, University of Notre Dame, IN 46556, USA. kyan@nd.edu

The British Journal of Mathematical and Statistical Psychology
|May 30, 2002
PubMed
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This study introduces a model segregation approach for structural equation modeling (SEM) to address common issues like non-convergence. The method allows fixing some parameters while estimating others, improving SEM practice.

Area of Science:

  • Statistics
  • Quantitative Psychology
  • Econometrics

Background:

  • Structural equation modeling (SEM) frequently encounters challenges such as improper solutions, non-convergence, differing variable distributions, and single-indicator latent variables.
  • These issues can impede reliable parameter estimation and model interpretation in complex statistical analyses.

Purpose of the Study:

  • To propose and investigate a novel model segregation approach for fitting structural equation models.
  • To provide a method for situations where fixing certain parameters is necessary for feasible estimation.

Main Methods:

  • Formulation of a model fitting process using a model segregation strategy.
  • Application of the theory of estimating equations and optimal estimating functions to study statistical properties.

Related Experiment Videos

  • Characterization of the dependency between new parameter estimates and prespecified parameter estimates.

    • Main Results:

    • The proposed model segregation approach offers a viable solution for common SEM problems.
    • The statistical properties of the procedure are rigorously examined using established estimation theories.
    • A dependency analysis reveals how estimates are influenced by fixed parameters.

    Conclusions:

    • The model segregation approach provides a robust framework for handling complex SEM scenarios.
    • A new rescaled model fit statistic is introduced to assess model adequacy.
    • The procedure demonstrates practical utility through illustrative examples.