Rate My Professor Sarah Depaoli

Sarah Depaoli is Professor of Quantitative Methods, Measurement, and Statistics and Department Chair of Psychological Sciences at the University of California, Merced. She earned her Ph.D. in Quantitative Methods, with a minor in Mathematical Statistics, from the University of Wisconsin-Madison in 2010 and joined UC Merced faculty in 2011, advancing to full professor and department chair in 2023. Her research specializes in Bayesian estimation techniques for latent variable models, structural equation modeling, finite mixture models, growth models, and longitudinal data. Key areas include prior distribution influences, robustness under varying priors, latent class recovery and separation, Bayesian methods for class enumeration and assignment, parameterization challenges in Bayesian SEM, and prior impacts on longitudinal analyses.

In 2021, Depaoli authored Bayesian Structural Equation Modeling (Guilford Press), offering systematic guidance on Bayesian SEM with examples, Mplus and R code, and reporting strategies. Her publications appear in leading journals like Structural Equation Modeling, Multivariate Behavioral Research, and Psychological Methods, including "Mixture class recovery in GMM under varying degrees of class separation" (2013), "Using Bayesian statistics for modeling PTSD through Latent Growth Mixture Modeling" (2015), "Improving transparency and replication in Bayesian statistics" (2017), and "The Importance of Prior Sensitivity Analysis in Bayesian Statistics" (2020). Recognized with the Association for Psychological Science Rising Star award (2016) and election to the Society of Multivariate Experimental Psychology (2016), she serves as Associate Editor for several journals: Multivariate Behavioral Research (since 2017), Psychological Methods (since 2018), and Journal of the Royal Statistical Society Series A (since 2022). Depaoli teaches undergraduate statistics and graduate quantitative methods courses.