Becker Friedman Institute

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Bayesian Exploratory Factor Analysis

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: 
Gabriella Conti, University College London
Sylvia Fruehwirth-Schnatter, University of Vienna
James J. Heckman, The University of Chicago
Rémi Piatek, Københavns Universitet
Publication Date: 
July, 2014
Publication Status: 
Document Number: 
2014-014
File: 
File Description: 
First version, July 2014