Psychological Modeling

Catalog description

In this seminar we will explore modeling techniques used in psychological science. Example analytic techniques that may be covered include: linear and non-linear models, agent-based modeling, network analysis, natural language processing, machine learning, systems modeling, dynamical/complex systems, or other computational/representative models. We will focus broadly on psychological science, meaning models will be applied to diverse areas (e.g., clinical, personality, social, health, I/O, behavioral neuroscience) but may have arisen in other fields (e.g., economics, mathematics, physics, computer science). Major assignments will include written papers, mathematical modeling, and a group based digital learning project. This course will use a variety of coding environments (e.g., NetLogo, R) so a willingness to learn how to program is expected but experience with coding is not required. Satisfies Psychology major requirement. Satisfies Data Science minor requirement. Satisfies Mathematical and Quantitative Thought requirement. Prerequisites & Notes Students should have previously completed PSY 101. A statistics course in any department is also recommended. Instructor permission may override the requirements.