Bayesian exploratory factor analysis

Gabriella Conti, Sylvia Frühwirth-Schnatter, James Heckman and Rémi Piatek
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Sylvia Frühwirth-Schnatter: Institute for Fiscal Studies and Vienna University of Economics and Business

No CWP30/14, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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.

Date: 2014-07-14
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Related works:
Journal Article: Bayesian exploratory factor analysis (2014)
Working Paper: Bayesian exploratory factor analysis (2014)
Working Paper: Bayesian Exploratory Factor Analysis (2014)
Working Paper: Bayesian Exploratory Factor Analysis (2014)
Working Paper: Bayesian Exploratory Factor Analysis (2014)
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