Bayesian Exploratory Factor Analysis

Gabriella Conti, Sylvia Fruehwirth-Schnatter (), James Heckman and Rémi Piatek
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Sylvia Fruehwirth-Schnatter: University of Vienna

No 2014-014, Working Papers from Human Capital and Economic Opportunity Working Group

Abstract: This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on \emph{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.

Keywords: Bayesian factor modeling; exploratory factor analysis; identifiability; marginal data augmentation; model expansion; model selection (search for similar items in EconPapers)
JEL-codes: C11 C38 C63 (search for similar items in EconPapers)
Date: 2014-07
New Economics Papers: this item is included in nep-ecm
Note: ECI
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Citations: View citations in EconPapers (46)

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http://humcap.uchicago.edu/RePEc/hka/wpaper/Conti_ ... explore-analysis.pdf First version, July 2014 (application/pdf)

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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