Bayesian Latent Variable Models for the Analysis of Experimental Psychology Data

24 Pages Posted: 12 Dec 2014 Last revised: 30 Nov 2016

See all articles by Edgar Merkle

Edgar Merkle

University of Missouri at Columbia - Department of Psychological Sciences

Ting Wang

University of Missouri at Columbia - Department of Psychological Sciences

Date Written: February 12, 2016

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.

Keywords: Bayesian factor analysis, Bayesian structural equation model, decision making

Suggested Citation

Merkle, Edgar and Wang, Ting, Bayesian Latent Variable Models for the Analysis of Experimental Psychology Data (February 12, 2016). Available at SSRN: https://ssrn.com/abstract=2536989 or http://dx.doi.org/10.2139/ssrn.2536989

Edgar Merkle (Contact Author)

University of Missouri at Columbia - Department of Psychological Sciences ( email )

28A McAlester Hall
Columbia, MO 65211
United States

Ting Wang

University of Missouri at Columbia - Department of Psychological Sciences ( email )

Columbia, MO 65211
United States

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