A Bayesian Regularized Approach to Exploratory Factor Analysis in One Step
Bibliographic Data
| ID | 21641841 |
|---|---|
| Authors | Jinsong Chen (0000-0002-0157-5469, University of Hong Kong, corresponding author) |
| Year | 2021 |
| Volume | 28 |
| Issue | 4 |
| Pages | 518-528 |
| Publication date | 2021-07-04 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Structural Equation Modeling: A Multidisciplinary Journal (JOURNAL) |
| Journal identifiers | ISSN: 1070-5511 • E-ISSN: 1532-8007 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10705511.2020.1854763 |
| OpenAlex | W3124960641 |
| Language | EN |
| Citations received | 4 |
| References cited | 35 |
This research proposes a one-step Bayesian regularized approach to exploratory factor analysis (EFA) with an unknown number of factors. The proposed Bayesian regularized exploratory factor analysis (BREFA) model builds on the idea of bi-level Bayesian sparse group selection and can produce exact zero estimates at both the factor and loading levels. It can distinguish true factors from spurious factors and provide estimations of model and tuning parameters simultaneously. In addition to achieving model simplicity at both the factor and item levels, the approach provides interval estimates that can be used for significance testing, making it capable of addressing both uncorrelated and correlated factors. The Bayesian hierarchical formulation is implemented using Markov chain Monte Carlo estimation with the multivariate spike and slab priors and posterior median estimator. Based on simulated and real data analysis, BREFA demonstrates clear advantages or flexibility compared with traditional and Bayesian EFA, in terms of factor extraction, parameter estimation, and model interpretation
Algorithm · Bayes factor · Bayesian inference · Bayesian probability · Estimator · Machine learning · Marginal likelihood · Markov chain Monte Carlo · Prior probability · Spurious relationship · Statistics · Advanced Statistical Modeling Techniques · Computer Science · Face and Expression Recognition · Gene expression and cancer classification · Mathematics · Artificial Intelligence
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| Unique citing works | 4 |
|---|---|
| Citations per year | 1 |
| Citation span | 2022 - 2025 (4) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 4 |