Methods for collecting psychologically relevant data and modeling these data are rapidly advancing. Bayesian statistical methods have emerged as a set of particularly useful techniques, both within cognitive psychology and more broadly in psychological science. It is thus important for psychology researchers to have a sound understanding of these methods.
In this honors thesis, I first identify pedagogical gaps in the teaching of Bayesian methods. I also describe the theoretical underpinnings of the Bayesian approach and provide justification for its use in psychological research. I then outline three prominent applied Bayesian methods that help illuminate psychological phenomena: Bayesian parameter estimation, Bayesian networks, and Bayesian cognitive modeling. Next, I detail my development of a set of online Bayesian methods tutorials designed to be accessible to those conducting psychology research. Finally, I present an empirical study of the educational value of a tutorial on Bayesian parameter estimation, as assessed by undergraduate (N = 100) and graduate (N = 5) students. Participants completed a pre-tutorial survey of their existing knowledge of statistical methods, read through the tutorial, and completed a post-tutorial survey reassessing their knowledge and evaluating their subjective perceptions of the tutorial. The findings suggest that students generally find the tutorial useful, especially the visual elements, and report that it increases their familiarity with relevant Bayesian concepts.
