Workshop overview

The use of Bayesian methods has grown quickly in recent years among researchers in the social, behavioral, and psychological sciences thanks to methodological and computational advances that have made their use feasible and practical. The Bayesian framework offers many advantages not available through conventional non-Bayesian approaches: among these include (1) the ability for researchers to incorporate their prior beliefs and expert knowledge into the analysis in a principled manner, (2) increased flexibility in terms of model specification, (3) usefulness in evaluating complex models with a small data set, and (4) straightforward interpretation of results and findings. The goal of this workshop is to offer an intuitive and practical introduction to the Bayesian framework. We do not assume previous exposure to Bayesian statistics; however, an understanding of basic statistical concepts is helpful.

Workshop objectives

This workshop will provide participants with an understanding of Bayesian statistics, the foundations underlying Bayesian analyses, and their practical applications in psychological research. By the end of the workshop, participants will:

  • Have an understanding of the similarities and differences between Bayesian and non-Bayesian approaches
  • Identify the basics of the Bayesian fundamentals
  • Gain experience with fitting simple Bayesian models using software and interpreting results

Course schedule and topics

The workshop is presented in two parts: Foundations and Applications. In Foundations, we focus on building the intuition underlying the Bayesian framework. In particular, we will discuss relevant concepts from probability theory, Bayes’ theorem (the main engine behind the Bayesian framework), and comparisons to frequentist approaches. In Applications, we will focus on the use of software to facilitate Bayesian analyses through a hands-on example from psychology with an emphasis on specifying models with software and interpretation of results.

Workshop instructors

Hyeri Hong is an assistant professor of research and statistics in the Department of Curriculum and Instruction and a core faculty member in the Educational Leadership Doctoral program at California State University, Fresno. She received her Ph.D. in Educational Measurement and Statistics from the University of Iowa. Dr. Hong teaches introductory statistics, advanced statistics, and measurement and evaluation courses. Her research interests include structural equation modeling, Bayesian estimation, and generalizability theory. She is leading Steve’s Scholars grant project as a P.I. with the support of Fresno Unified School District. Her collaborations have been published in high-impact journals like Psychological Methods, Psychological Assessment, Psych, Journal of Personality Assessment, Journal of Experimental Education, Structural Equation Modeling: A Multidisciplinary Journal, etc.



Alfonso J. Martinez is a PhD candidate (ABD) in Psychological and Quantitative Foundations at the University of Iowa specializing in quantitative methods and statistics. He obtained his BA in Psychology from California State University, Fresno, his MA in Psychological and Quantitative Foundations, and his MS in Statistics, both from the University of Iowa. In Fall 2024, he will join the Department of Psychology at Fordham University as an Assistant Professor of Psychometrics and Quantitative Psychology. His research focuses on the development, evaluation, and application of novel psychometric, statistical, and computational methods for the social, behavioral, and psychological sciences. His areas of expertise are in generalized latent variable and psychometric modeling (e.g., structural equation modeling, item response theory), Bayesian inference, longitudinal data analysis, measurement invariance, probabilistic graphical models, mixture modeling, and computational statistics. His methodological research has been published and featured in top quantitative methods journals including Multivariate Behavioral Research, Psych, and British Journal of Mathematical and Statistical Psychology.

Official workshop playlist

A playlist of songs that provided inspiration during the development of this workshop.