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' abstract)
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:4521 |
| Date | 27 June 2014 |
| Creators | Conti, Gabriella, Frühwirth-Schnatter, Sylvia, Heckman, James J., Piatek, Rémi |
| Publisher | Elsevier |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Article, PeerReviewed |
| Format | application/pdf |
| Relation | http://dx.doi.org/10.1016/j.jeconom.2014.06.008, http://www.elsevier.com/, http://epub.wu.ac.at/4521/ |
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