Bayesian Exploratory Factor Analysis via Gibbs Sampling

Adrian Quintero, Emmanuel Lesaffre and Geert Verbeke
Additional contact information
Adrian Quintero: Icfes-Colombian Institute for Educational Evaluation
Geert Verbeke: I-BioStat, KU Leuven

Journal of Educational and Behavioral Statistics, 2024, vol. 49, issue 1, 121-142

Abstract: Bayesian methods to infer model dimensionality in factor analysis generally assume a lower triangular structure for the factor loadings matrix. Consequently, the ordering of the outcomes influences the results. Therefore, we propose a method to infer model dimensionality without imposing any prior restriction on the loadings matrix. Our approach considers a relatively large number of factors and includes auxiliary multiplicative parameters, which may render null the unnecessary columns in the loadings matrix. The underlying dimensionality is then inferred based on the number of nonnull columns in the factor loadings matrix, and the model parameters are estimated with a postprocessing scheme. The advantages of the method in selecting the correct dimensionality are illustrated via simulations and using real data sets.

Keywords: Gibbs sampling; model dimensionality; ordering dependence; sparsity; spike-slab prior (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.3102/10769986231176023 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:49:y:2024:i:1:p:121-142

DOI: 10.3102/10769986231176023

Access Statistics for this article

More articles in Journal of Educational and Behavioral Statistics
Bibliographic data for series maintained by SAGE Publications ().