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Andres F Perez Alonso1, Yves Rosseel2, Jeroen K Vermunt1

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Mixture multigroup structural equation modeling (MMG-SEM) clusters groups by shared structural relations, even with measurement noninvariance. This method ensures valid comparisons of latent variable relationships across diverse populations.

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Area of Science:

  • Behavioral Science
  • Psychometrics
  • Quantitative Psychology

Background:

  • Structural equation modeling (SEM) is standard for examining latent variable relations.
  • Comparing structural relations across many groups is common, but differences and similarities (clusters) exist.
  • Measurement invariance is crucial for valid cross-group comparisons, yet often violated.

Purpose of the Study:

  • To introduce Mixture Multigroup Structural Equation Modeling (MMG-SEM) for clustering groups based on structural relations.
  • To address the challenge of measurement noninvariance when comparing structural relations across multiple groups.
  • To provide a method that ensures valid clustering unaffected by measurement differences.

Main Methods:

  • Proposes an estimation procedure for MMG-SEM using the R package "lavaan".
  • Employs cluster-specific structural relations and group-specific measurement parameters.
  • Evaluates performance through two simulation studies.

Main Results:

  • MMG-SEM successfully recovers group clusters based on structural relations.
  • The method accurately identifies cluster-specific structural relations.
  • Partially group-specific measurement parameters are effectively captured.

Conclusions:

  • MMG-SEM provides a valid approach to clustering groups by structural relations, accounting for measurement noninvariance.
  • The method enhances the accuracy of cross-group comparisons in behavioral science.
  • Empirical application demonstrates MMG-SEM's utility in cross-cultural research.