Journal of Educational and Behavioral Statistics· 2026Q1
Sparse Bayesian Joint Modal Estimation for Exploratory Item Factor Analysis
- 0citations
- 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.
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 ↗
The rest is in the Pofolia app
Takeaways, key points and questions to the paper; new summaries every day for your field. Free.
Sign in on the web to openField: Information Systems
Information SystemsComputer Science