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Selecting scaling indicators in structural equation models (sems).

Kenneth A Bollen1, Adam G Lilly2, Lan Luo1

  • 1Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill.

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Choosing a scaling indicator for latent variables in structural equation modeling (SEM) is crucial. This study provides guidelines and diagnostic tools for selecting the best indicator to ensure accurate measurement of psychological constructs.

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Area of Science:

  • Psychology
  • Quantitative Psychology
  • Psychometrics

Background:

  • Latent variables are essential in psychology for measuring abstract constructs.
  • Structural Equation Modeling (SEM) commonly uses a reference indicator for scaling latent variables.
  • The selection of this scaling indicator often lacks rigorous consideration.

Purpose of the Study:

  • To demonstrate the necessity of proper scaling for latent variables.
  • To propose evidence-based criteria and diagnostic tools for selecting scaling indicators.
  • To guide researchers in making informed decisions for improved measurement accuracy.

Main Methods:

  • Review of structural equation modeling (SEM) practices.
  • Development of criteria for ideal scaling indicators (e.g., high validity, factor complexity).
  • Application of diagnostic tools to empirical examples.

Main Results:

  • Identified key criteria for selecting optimal scaling indicators.
  • Demonstrated the utility of proposed diagnostics with real-world data.
  • Provided a framework for evaluating and navigating conflicting criteria.

Conclusions:

  • Informed selection of scaling indicators enhances the reliability of latent variable measurement in SEM.
  • The proposed criteria and diagnostics offer practical guidance for researchers.
  • Adopting these guidelines can lead to more robust and valid psychological models.