A tutorial on Bayesian structural equation modelling: Principles and applications

Area of Science:

• Psychology
• Statistics
• Quantitative Research Methods

Background:

• Frequentist methods in structural equation modeling can be limited with complex models and small sample sizes.
• Bayesian structural equation modeling (BSEM) presents a viable alternative with distinct advantages.
• Understanding BSEM is crucial for advancing psychological research methodologies.

Purpose of the Study:

• To explore the utilization of Bayesian structural equation modelling (BSEM) in psychology.
• To highlight BSEM's advantages over frequentist approaches for complex models and small sample sizes.
• To provide researchers with practical guidance and novel ideas for empirical research using BSEM.

Main Methods:

• Introduction to fundamental BSEM concepts: prior setting, model convergence, and model fit evaluation.
• Illustrative examples of commonly employed BSEM applications: confirmatory factor analysis (CFA), mediation models, and multigroup CFA.
• Provision of empirical data and computer codes to facilitate the practical implementation of BSEM.

Main Results:

• BSEM effectively handles complex models and small sample sizes, offering advantages over frequentist methods.
• The paper provides a clear framework for understanding and applying BSEM in psychological research.
• Illustrative examples and code facilitate the adoption of BSEM techniques.

Conclusions:

• BSEM is a powerful tool for psychological researchers, enabling the analysis of complex models and small datasets.
• The continued development of BSEM promises improved methods, techniques, and reporting standards.
• Researchers are equipped to overcome challenges associated with traditional statistical methods through BSEM adoption.