A tutorial on Bayesian structural equation modelling
Principles and applications
Bibliographic Data
| ID | 21336751 |
|---|---|
| Authors | Qijin Chen (0000-0003-0273-9514, Department of Psychology Sun Yat‐Sen University Guangzhou P. R. China), Kun Su (0000-0002-7793-1853, Department of Psychology Sun Yat‐Sen University Guangzhou P. R. China), Yonglin Feng (Department of Psychology Sun Yat‐Sen University Guangzhou P. R. China), Lijin Zhang (0000-0001-8172-2977, Graduate School of Education Stanford University Stanford CA USA), R Ding (0000-0002-0151-2548, Department of Psychology Sun Yat‐Sen University Guangzhou P. R. China), Junhao Pan (0000-0001-7156-6077, Department of Psychology Sun Yat‐Sen University Guangzhou P. R. China, corresponding author) |
| Year | 2024 |
| Volume | 59 |
| Issue | 6 |
| Pages | 1326-1346 |
| Publication date | 2024-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Psychology (JOURNAL) |
| Journal identifiers | ISSN: 0020-7594 • E-ISSN: 1464-066X |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/ijop.13258 |
| PMID | 39389756 |
| OpenAlex | W4403287099 |
| Language | EN |
| Citations received | 2 |
| References cited | 54 |
This paper explores the utilisation of Bayesian structural equation modelling (BSEM) in psychology, highlighting its advantages over frequentist methods for handling complex models and small sample sizes. Basic concepts and fundamental issues relevant to BSEM are introduced, such as prior setting, model convergence, and model fit evaluation and so on. The paper also provides illustrative examples of commonly employed BSEMs, including confirmatory factor analysis (CFA) models, mediation models and multigroup CFA models, accompanied by empirical data and computer codes to facilitate implementation. Our goal is to provide researchers with novel ideas for empirical research and equip them to overcome challenges inherent to traditional methods. As BSEM continues to gain traction in various fields, we anticipate its development will feature improved methods, techniques and reporting standards
Bayesian inference · Bayesian probability · Confirmatory factor analysis · Data science · Frequentist inference · Machine learning · Management science · Mediation · Structural equation modeling · Advanced Statistical Modeling Techniques · Artificial Intelligence · Computer Science · Mental Health Research Topics · Psychometric Methodologies and Testing
Measurement invariance
Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)
A weakly informative default prior distribution for logistic and other regression models
Bayesian structural equation modeling
Bayesian Versus Frequentist Estimation for Structural Equation Models in Small Sample Contexts
A General Approach to Confirmatory Maximum Likelihood Factor Analysis
Multiple-Group Factor Analysis Alignment
Measurement Invariance Testing with Many Groups
Bayesian mediation analysis.
A Gentle Introduction to Bayesian Analysis
A Review and Synthesis of the Measurement Invariance Literature
Bayes Factors
Mediation
Cutoff criteria for fit indexes in covariance structure analysis
Species Richness of Parasite Assemblages
Inference from Iterative Simulation Using Multiple Sequences
Exploratory Structural Equation Modeling
Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models
A checklist for testing measurement invariance
Structural Equations with Latent Variables
Bayesian Multilevel Structural Equation Modeling
Advances in Bayesian Model Fit Evaluation for Structural Equation Models
Evaluating Model Fit in Bayesian Confirmatory Factor Analysis With Large Samples
Bayesian Factor Analysis as a Variable-Selection Problem
Evaluation of the Bayesian and Maximum Likelihood Approaches in Analyzing Structural Equation Models with Small Sample Sizes
Coping patterns as predictors of burnout
Burnout
Model modifications in covariance structure analysis
| Unique citing works | 2 |
|---|---|
| Citations per year | 2 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |