Dr. Sarah Depaoli

Associate Professor

Sarah Depaoli received her doctorate in Quantitative Methods from the University of Wisconsin, Madison in 2010, with a minor in mathematical statistics. She is a faculty member in Quantitative Psychology at the University of California, Merced. Her main program of research surrounds the use of Bayesian statistics to improve issues in latent variable modeling. She is particularly interested in using prior distributions to aid in the estimation of various latent variable models, including latent growth (mixture) models, structural equation models, and multilevel structural equation models. Her work includes: examining the performance of different forms of priors, developing new specifications and methods for deriving priors, and applying Bayesian methods to empirical problems (typically in health-related fields). The main goal of her work is to improve the use of Bayesian statistics and latent variable models through methodological developments and by disseminating her findings into the applied literature. Her academic website is: www.sarahdepaoli.com