title: Bayesian latent variable models for the analysis of experimental psychology data.
abstract: In this paper, we address the use of Bayesian factor analysis and structural equation models to draw inferences from experimental psychology data. While such application is non-standard, the models are generally useful for the unified analysis of multivariate data that stem from, e.g., subjects' responses to multiple experimental stimuli. We first review the models and the parameter identification issues inherent in the models. We then provide details on model estimation via JAGS and on Bayes factor estimation. Finally, we use the models to re-analyze experimental data on risky choice, comparing the approach to simpler, alternative methods.
authors.affiliation: University of Missouri, Columbia, MO, USA. merklee@missouri.edu.
authors.forename: Edgar C
authors.initials: EC
authors.lastname: Merkle
authors.affiliation: University of Missouri, Columbia, MO, USA.
authors.forename: Ting
authors.initials: T
authors.lastname: Wang
mesh_terms: D001499:Bayes Theorem; D002755:Choice Behavior; D005163:Factor Analysis, Statistical; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D011584:Psychology; D011589:Psychology, Experimental; D012306:Risk
keywords: Bayesian statistics; Decision making