title: Dealing with Reflection Invariance in Bayesian Factor Analysis.
abstract: This paper considers the reflection unidentifiability problem in confirmatory factor analysis (CFA) and the associated implications for Bayesian estimation. We note a direct analogy between the multimodality in CFA models that is due to all possible column sign changes in the matrix of loadings and the multimodality in finite mixture models that is due to all possible relabelings of the mixture components. Drawing on this analogy, we derive and present a simple approach for dealing with reflection in variance in Bayesian factor analysis. We recommend fitting Bayesian factor analysis models without rotational constraints on the loadings-allowing Markov chain Monte Carlo algorithms to explore the full posterior distribution-and then using a relabeling algorithm to pick a factor solution that corresponds to one mode. We demonstrate our approach on the case of a bifactor model; however, the relabeling algorithm is straightforward to generalize for handling multimodalities due to sign invariance in the likelihood in other factor analysis models.
authors.affiliation: Department of Statistics, University of Washington, Box 354320, Seattle, WA, 98195, USA. erosheva@u.washington.edu.
authors.forename: Elena A
authors.initials: EA
authors.lastname: Erosheva
authors.affiliation: Department of Statistics, University of Washington, Box 354320, Seattle, WA, 98195, USA.
authors.forename: S McKay
authors.initials: SM
authors.lastname: Curtis
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D005163:Factor Analysis, Statistical; D006801:Humans; D008390:Markov Chains; D009010:Monte Carlo Method; D011594:Psychometrics
keywords: Markov chain Monte Carlo; identifiability constraints; label-switching; relabeling; rotation; rotational invariance