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An introduction to structural equation models.

D J Francis1

  • 1Department of Psychology, University of Houston, TX 77204-5341.

Journal of Clinical and Experimental Neuropsychology
|October 1, 1988
PubMed
Summary
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Structural equation modeling (SEM) offers advanced statistical techniques to enhance neuropsychological research. This paper guides researchers in applying SEM for improved theory and practice.

Area of Science:

  • Neuropsychology
  • Statistics
  • Psychometrics

Background:

  • Structural Equation Models (SEM) are powerful statistical tools.
  • Advancing neuropsychological theory and practice requires advanced analytical methods.

Purpose of the Study:

  • To provide an overview of SEM, including model classes, terminology, and diagrams.
  • To outline the application steps for SEM in research.
  • To discuss model fit assessment, significance testing, and the advantages/disadvantages of SEM in neuropsychology.

Main Methods:

  • Overview of SEM classes and associated terminology/diagrams.
  • Step-by-step guide to applying SEM to research problems.
  • Explanation of model fit assessment and significance testing in SEM.

Related Experiment Videos

Main Results:

  • Researchers can better assess the relevance of SEM to their work.
  • Guidance is provided to facilitate the successful application of SEM.

Conclusions:

  • SEM has significant potential to advance neuropsychological theory and practice.
  • This paper serves as a foundational resource for researchers interested in applying SEM.