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This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements.
Authors
Research Associate University of Chicago
James is a Research Associate of the IFS and the Henry Schultz Distinguished Service Professor of Economics at the University of Chicago.
University of Strasbourg
Research Fellow University College London
Gabriella is a Research Fellow of the IFS and a Professor of Economics in the Department of Economics and in the Department of Social Science at UCL.
Sylvia Frühwirth-Schnatter
Working Paper details
- DOI
- 10.1920/wp.cem.2014.3014
- Publisher
- Cemmap
Suggested citation
Conti, G et al. (2014). Bayesian exploratory factor analysis. London: Cemmap. Available at: https://ifs.org.uk/publications/bayesian-exploratory-factor-analysis (accessed: 18 April 2024).
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