title: Bayesian Exploratory Factor Analysis.
abstract: This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves onad hocclassical 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.affiliation: Department of Applied Health Research, University College London, UK.
authors.forename: Gabriella
authors.initials: G
authors.lastname: Conti
authors.affiliation: Vienna University of Economics and Business, Austria.
authors.forename: Sylvia
authors.initials: S
authors.lastname: Frühwirth-Schnatter
authors.affiliation: Department of Economics, University of Chicago, USA ; American Bar Foundation, USA.
authors.forename: James J
authors.initials: JJ
authors.lastname: Heckman
authors.affiliation: Department of Economics, University of Copenhagen, Denmark.
authors.forename: Rémi
authors.initials: R
authors.lastname: Piatek
keywords: Bayesian Factor Models; Exploratory Factor Analysis; Identifiability; Marginal Data Augmentation; Model Expansion; Model Selection