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Comparing Bayesian estimation and structural-after-measurement approaches for structural equation models with latent

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Summary
This summary is machine-generated.

Structural Equation Models (SEMs) with latent interactions benefit from Bayesian and Structural-After-Measurement (SAM) approaches. SAM methods demonstrate superior versatility and adaptability across various complex SEMs compared to Bayesian approaches.

Keywords:
Bayesian estimationLatent interactionMultilevel structural equation modelPartially nested dataStructural-after-measurement (SAM) approaches

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Area of Science:

  • Statistics
  • Psychometrics
  • Quantitative Psychology

Background:

  • Conventional estimators face limitations in structural equation models (SEMs) with latent interactions.
  • Bayesian and Structural-After-Measurement (SAM) approaches offer potential solutions, particularly in small-sample studies.
  • Limited systematic comparisons exist regarding the benefits and trade-offs of these advanced SEM approaches.

Purpose of the Study:

  • To compare the performance of Bayesian and SAM estimators in multilevel SEMs with different types of latent interactions (within-, between-, cross-level).
  • To investigate the adaptability of SAM approaches in partially nested SEMs with latent moderated mediation.
  • To identify estimator suitability based on SEM complexity and data structure.

Main Methods:

  • Comparative performance analysis of Bayesian and SAM estimators.
  • Application of SAM approaches to multilevel SEMs with various latent interactions.
  • Extension and evaluation of SAM in partially nested SEMs involving latent moderated mediation.

Main Results:

  • Significant differences in estimator performance were observed, contingent on the type of latent interaction.
  • SAM approaches exhibited robust performance across diverse latent interactions in multilevel and partially nested SEMs.
  • Bayesian approaches encountered difficulties with cross-level latent interactions and were less adaptable to partially nested SEMs.

Conclusions:

  • SAM approaches present a versatile and adaptable alternative or complement to traditional SEM estimators.
  • The choice of estimator should consider the specific SEM type, the nature of the latent interaction, and the data structure.
  • SAM methods are particularly advantageous for complex multilevel and partially nested SEMs with latent interactions.