Journal of Educational and Behavioral Statistics· 2026Q1
Sparse Bayesian Joint Modal Estimation for Exploratory Item Factor Analysis
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- Q1SCImago
- 2026year
Short summary
A new scalable Bayesian algorithm, Sparse BJME, efficiently and accurately estimates factor loadings and selects relevant variables in exploratory item factor analysis, even for large datasets.
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Key points
- Introduces Sparse BJME, a scalable Bayesian algorithm for exploratory item factor analysis.
- BJME jointly estimates model parameters and factor scores by maximizing the complete-data joint posterior density.
- The algorithm uses an alternating optimization scheme for scalability.
- Simulation studies show high computational efficiency and accuracy in variable selection and parameter recovery.
- Successfully applied to a large-scale psychological assessment dataset, extracting interpretable factor loadings.
AI-generated from the title and abstract; the full text is not read.
Abstract
This study presents a scalable Bayesian estimation algorithm for sparse estimation in exploratory item factor analysis based on a classical Bayesian estimation method, namely Bayesian joint modal estimation (BJME). BJME jointly estimates the model parameters and factor scores by maximizing the complete-data joint posterior density. The proposed algorithm achieves scalability through an alternating optimization scheme that iteratively updates the model parameters and factor scores. Simulation studies show that the proposed algorithm has high computational efficiency and high accuracy in variable selection over latent factors and parameter recovery. Moreover, we provide a real data analysis using a large-scale dataset from a psychological assessment of the Big Five personality traits. The results indicate that the proposed algorithm extracts an interpretable factor loading structure in a computationally efficient manner.
The authors' abstract, as published at the source. Journal of Educational and Behavioral Statistics, 2026 · DOI ↗
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Field: Information Systems
Information SystemsComputer Science