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On measures of explained variance in nonrecursive structural equation models.

P M Bentler1, T Raykov

  • 1Department of Psychology, University of California, Los Angeles 90095-1563, USA. bentler@ucla.edu

The Journal of Applied Psychology
|March 31, 2000
PubMed
Summary

This study introduces a new method to calculate explained variance in nonrecursive structural equation models. This approach addresses limitations of standard R2 measures in complex models with reciprocal relationships.

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Area of Science:

  • Statistics
  • Econometrics
  • Psychometrics

Background:

  • Standard R2 measures are insufficient for nonrecursive structural equation models.
  • Nonrecursive models feature reciprocal interdependencies among variables, complicating variance explanation.
  • Existing methods lack a general approach for defining explained variance in these complex models.

Purpose of the Study:

  • To provide a general method for defining and estimating explained variance in latent dependent variables.
  • To address the limitations of R2 in nonrecursive linear structural equation models.
  • To introduce a practical estimation technique for researchers.

Main Methods:

  • Developed a general approach to define variance explained in latent variables.
  • Proposed a novel estimation method for this definition.

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  • Demonstrated the method's implementation in statistical software (EQS, LISREL).

    • Main Results:

    • A generalizable method for calculating explained variance in nonrecursive models was established.
    • The proposed estimation technique is practical and implementable.
    • The method is shown to be effective in EQS and LISREL software.

    Conclusions:

    • The new approach successfully defines and estimates explained variance in nonrecursive models.
    • This method enhances the analytical capabilities for complex structural equation models.
    • Researchers can now more accurately assess model fit and variable influence in reciprocal systems.