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Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE
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Structural equation models and the quantification of behavior.

Kenneth A Bollen1, Mark D Noble

  • 1Department of Sociology, University of North Carolina, Chapel Hill, NC 27599, USA. bollen@unc.edu

Proceedings of the National Academy of Sciences of the United States of America
|July 7, 2011
PubMed
Summary
This summary is machine-generated.

Structural equation models (SEMs) address measurement errors in behavioral research. This overview explains latent variable SEMs, their equations, and modeling steps for quantifying complex behavioral relationships.

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Area of Science:

  • Social and Behavioral Sciences
  • Quantitative Psychology
  • Educational Measurement

Background:

  • Behavioral research frequently uses variables with measurement errors.
  • Complex relationships among variables necessitate multiequation models.
  • Existing methods may not adequately address measurement error in behavioral quantification.

Purpose of the Study:

  • To provide an overview of latent variable Structural Equation Models (SEMs).
  • To explain the fundamental equations and modeling steps in SEMs.
  • To illustrate the application of SEMs with practical examples.

Main Methods:

  • Latent variable Structural Equation Models (SEMs).
  • Presentation of SEM equations and modeling procedures.
  • Illustrative case studies of SEM application.

Main Results:

  • SEMs effectively handle multiequation models and measurement error.
  • The general SEM framework accommodates diverse behavioral quantification problems.
  • Successful application demonstrated through three distinct illustrations.

Conclusions:

  • Latent variable SEMs are a powerful tool for behavioral research.
  • Researchers with hypothesis formulation knowledge can leverage SEMs.
  • SEMs offer a robust approach to quantifying behavior with complex variable relationships.