Overview of Structural Equation Modeling Concepts

Oct 27, 2024

Structural Equation Modeling Overview

Introduction

  • Structural Equation Modeling (SEM) is an analytical approach for testing theories.
  • Combines confirmatory factor analyses and multiple regressions.
  • Aimed at providing a high-level conceptual understanding of SEM.

Presenter's Background

  • Ashley, a second-year PhD student, creates videos about goals and academics.
  • Learning by teaching approach.

Structure of the Video

  • Focus on Anderson and Gerbing's 1988 article as a foundation for the two-step SEM approach.
  • Incorporation of contemporary best practices.

Two-Step SEM Approach

Step 1: Analyze the Measurement Model

  • Measurement Model: Tests if we measure what we intend.
  • Involves using Confirmatory Factor Analyses (CFA).
  • Types of Measurement Invariance Tests:
    • Configural Invariance: Tests the factor structure (e.g., two-factor vs. three-factor).
    • Metric Invariance: Evaluates the expected factor loadings.
      • Aim for factor loadings above 0.5 for strength.
  • Analyze measurement models independently for exogenous (X) and endogenous (Y) variables.

Theoretical Framework

  • Utilize theory to re-specify the measurement model (e.g., allowing residuals to co-vary).
  • Importance of guiding analyses with strong theoretical backing.
  • Avoid "harking" (hypothesizing after results) to maintain scientific integrity.

Reporting and Replication

  • Report degrees of freedom for all models tested to facilitate replication.
  • Replication is essential for durable knowledge creation.

Step 2: Analyze the Path Model

  • Path Model: Represents relationships between variables.
  • Hypothesize the direction of relationships (e.g., X leads to Y).
    • Analyze the statistical significance and coefficient direction.
  • Ensure all connections in the model are tested and theoretically justified.

Conclusion

  • Importance of measuring and justifying all aspects of both models.
  • Encourages a learning mindset; failures are opportunities to build knowledge.
  • Invitation to explore further resources, such as Karma for deeper understanding of methods.

Call to Action

  • Encourage feedback and engagement from the community.
  • Promote further discussions on structural equation modeling.