A Generalized Partially Confirmatory Factor Analysis Framework with Mixed Bayesian Lasso Methods
Bibliographic Data
| ID | 19291342 |
|---|---|
| Authors | Jinsong Chen (0000-0002-0157-5469, Faculty of Education, The University of Hong Kong, corresponding author) |
| Year | 2022 |
| Volume | 57 |
| Issue | 6 |
| Pages | 879-894 |
| Publication date | 2022-11-02 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Multivariate Behavioral Research (JOURNAL) |
| Journal identifiers | ISSN: 0027-3171 • E-ISSN: 1532-7906 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00273171.2021.1925520 |
| PMID | 34003705 |
| OpenAlex | W3162386332 |
| Language | EN |
| Citations received | 5 |
| References cited | 31 |
This research extends the partially confirmatory approach to accommodate mixed types of data and missingness in a unified framework that can address a wide range of the confirmatory-exploratory continuum in factor analysis. A mix of Bayesian adaptive and covariance Lasso procedures was developed to estimate model parameters and regularize the loading structure and local dependence simultaneously. Several model variants were offered with different constraints for identification. The less-constrained variant can achieve sufficient condition for the more-powerful variant, although loading estimates associated with local dependence can be inflated. Parameter recovery was satisfactory, but the information on local dependence was partially lost with categorical data or missingness. A real-life example illustrated how the models can be used to obtain a more discernible loading pattern and to identify items that do not measure what they are supposed to measure. The proposed methodology has been implemented in the R package LAWBL
Bayesian probability · Categorical variable · Confirmatory factor analysis · Covariance · Data mining · Econometrics · Machine learning · Measure (data warehouse) · Missing data · Statistics · Structural equation modeling · Artificial Intelligence · Computer Science · Mathematics · Spatial and Panel Data Analysis · Statistical Methods and Bayesian Inference · Statistical Methods and Inference
Regularized Variational Approximation for Partially Confirmatory Factor Analysis
Partially Confirmatory Approach to Factor Analysis with Bayesian Learning
Alternative Bayesian Regularizations for Factor Correlations and Loadings
Research Design and Model Estimation Under the Partially Confirmatory Latent Variable Modeling Framework with Multi-Univariate Bayesian Lassos
Regularized Variational Bayesian Approximations for Variable Selection in Extended Multiple-Indicators Multiple-Causes Models
Structural Equation Modeling
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
The Bayesian Lasso
The Calculation of Posterior Distributions by Data Augmentation
Ridge Regression
Monte Carlo sampling methods using Markov chains and their applications
Bayesian structural equation modeling
Latent variable structural equation modeling with categorical data
The Adaptive Lasso and Its Oracle Properties
Estimation of IRT graded response models
Equation of State Calculations by Fast Computing Machines
Regression Shrinkage and Selection Via the Lasso
Bayesian Factor Analysis as a Variable-Selection Problem
| Unique citing works | 5 |
|---|---|
| Citations per year | 1.25 |
| Citation span | 2022 - 2026 (5) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 5 |