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Bayesian exploratory factor analysis

Authors: Gabriella Conti , Sylvia Frühwirth-Schnatter , James Heckman and Rémi Piatek
Date: 14 July 2014
Type: cemmap Working Paper, CWP30/14
DOI: 10.1920/wp.cem.2014.3014

Abstract

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 identifi cation criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confi rms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements.

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Gabriella Conti, Sylvia Frühwirth-Schnatter, James Heckman and Rémi Piatek November 2014, Bayesian exploratory factor analysis, Journal article, Elsevier

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