Arbeitspapier

Bayesian Exploratory Factor Analysis

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.

Language
Englisch

Bibliographic citation
Series: NRN Working Paper, NRN: The Austrian Center for Labor Economics and the Analysis of the Welfare State ; No. 1408

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

Event
Geistige Schöpfung
(who)
Conti, Gabriella
Frühwirth-Schnatter, Sylvia
Heckman, James J.
Piatek, Rémi
Event
Veröffentlichung
(who)
Johannes Kepler University Linz, NRN - The Austrian Center for Labor Economics and the Analysis of the Welfare State
(where)
Linz
(when)
2014

Handle
Last update
10.03.2025, 11:44 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Conti, Gabriella
  • Frühwirth-Schnatter, Sylvia
  • Heckman, James J.
  • Piatek, Rémi
  • Johannes Kepler University Linz, NRN - The Austrian Center for Labor Economics and the Analysis of the Welfare State

Time of origin

  • 2014

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