Bayesian exploratory factor analysis

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: cemmap working paper ; No. CWP30/14

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models ; Computational Techniques; Simulation Modeling
• Subject: Bayesian Factor Models ; Exploratory Factor Analysis ; Identifiability ; Marginal Data Augmentation ; Model Expansion ; Model Selection

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Conti, Gabriella ; Frühwirth-Schnatter, Sylvia ; Heckman, James ; Piatek, Rémi
• Event: Veröffentlichung
• (who): Centre for Microdata Methods and Practice (cemmap)
• (where): London
• (when): 2014

Further information

• DOI: doi:10.1920/wp.cem.2014.3014
• Handle: http://hdl.handle.net/10419/111371
• Last update: 10.03.2025, 11:44 AM CET