Bayesian exploratory factor analysis
Copyright policy )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.
- University of Chicago United States
- University College London United Kingdom
- Vienna University of Economics and Business Austria
- Johannes Kepler University of Linz Austria
- Department of Economics University of Copenhagen Denmark
model selection, Bayesian inference, Bayesian factor models, Faculty of Social Sciences, Bayesian Factor Models, Bayesian Factor Models / Exploratory Factor Analysis / Identifiability / Marginal Data Augmentation / Model Expansion / Model Selection, /dk/atira/pure/core/keywords/FacultyOfSocialSciences, Model Expansion, Identifiability, model expansion, C38, C11, Model Selection, exploratory factor analysis, ddc:330, Factor analysis and principal components; correspondence analysis, identifiability, Bayesian factor modeling, exploratory factor analysis, identifiability, marginal data augmentation, model expansion, model selection, JEL C11, C38, C63, Bayesian Factor Models; Exploratory Factor Analysis; Identifiability; Marginal Data Augmentation; Model Expansion; Model Selection., C63, Exploratory Factor Analysis, marginal data augmentation, identifiability, exploratory factor analysis, Bayesian factor models, model expansion, model selection, Marginal Data Augmentation, bayesian factor modeling, exploratory factor analysis, identifiability, marginal data augmentation, model expansion, model selection, marginal data augmentation, jel: jel:C63, jel: jel:C11, jel: jel:C38
model selection, Bayesian inference, Bayesian factor models, Faculty of Social Sciences, Bayesian Factor Models, Bayesian Factor Models / Exploratory Factor Analysis / Identifiability / Marginal Data Augmentation / Model Expansion / Model Selection, /dk/atira/pure/core/keywords/FacultyOfSocialSciences, Model Expansion, Identifiability, model expansion, C38, C11, Model Selection, exploratory factor analysis, ddc:330, Factor analysis and principal components; correspondence analysis, identifiability, Bayesian factor modeling, exploratory factor analysis, identifiability, marginal data augmentation, model expansion, model selection, JEL C11, C38, C63, Bayesian Factor Models; Exploratory Factor Analysis; Identifiability; Marginal Data Augmentation; Model Expansion; Model Selection., C63, Exploratory Factor Analysis, marginal data augmentation, identifiability, exploratory factor analysis, Bayesian factor models, model expansion, model selection, Marginal Data Augmentation, bayesian factor modeling, exploratory factor analysis, identifiability, marginal data augmentation, model expansion, model selection, marginal data augmentation, jel: jel:C63, jel: jel:C11, jel: jel:C38
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).90 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!