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Related Experiment Videos

Covariance structure analysis: statistical practice, theory, and directions.

P M Bentler1, P Dudgeon

  • 1Department of Psychology, University of California, Los Angeles, Box 951563, Los Angeles, CA 90095-1563, USA.

Annual Review of Psychology
|January 1, 1996
PubMed
Summary
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Covariance structure analysis requires careful consideration of statistical assumptions for valid results. This review clarifies when different estimation methods yield correct conclusions, even with data misspecification.

Area of Science:

  • Statistics
  • Quantitative Psychology
  • Econometrics

Background:

  • Covariance structure analysis is increasingly used for nonexperimental data.
  • Proper statistical requirements for its application are often overlooked.
  • Model adequacy conclusions depend on appropriate estimation procedures.

Purpose of the Study:

  • To review the statistical requirements for valid covariance structure analysis.
  • To examine conditions for correct conclusions from various estimation methods.
  • To illustrate the consequences of ignoring these conditions.

Main Methods:

  • Analogies to linear regression and ANOVA are used for explanation.
  • A distinction is made between correctly and incorrectly specified estimation methods.

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  • A brief example is provided for illustration.

    • Main Results:

    • Valid conclusions are possible even when the data distribution is misspecified.
    • Ignoring statistical requirements can lead to incorrect model adequacy assessments.
    • The study clarifies the impact of estimation method appropriateness.

    Conclusions:

    • Researchers must understand the statistical assumptions of estimation methods in covariance structure analysis.
    • Awareness of conditions for correct conclusions enhances the validity of model fitting.
    • Accessible computer code is provided for advanced methods.