Research Design and Statistics 1 (PSY 200)
This course will introduce students to scientific thinking, with a specific focus on how scientific understanding manifests in psychology, neuroscience, and interdisciplinary research across the social sciences. In this foundational course, students will develop skills important for advanced PSY courses (e.g., consumption, evaluation, and communication of data in various forms). Course content will address the following questions in the context of psychological science: 1) What is scientific knowledge, 2) How do we design, test, and formalize research questions, and 3) Current issues (e.g., ethics, replicability, diversity/inclusion/belonging). Emphasis will be placed on interpreting and communicating psychological science (visual and oral presentation of data, APA style, etc.). Students will be introduced to quantitative analysis and statistical programming. In this course, students will develop knowledge of historical and current systems of bias that arise in psychological science, as well as develop skills to design and critique psychological research using a critical lens. Furthermore, students will learn how to reduce bias through methodology, design, and statistical thinking. Lastly, students will practice reimagining scientific inquiry from an inclusive framework, contextualizing the way that psychology asks questions in comparison to other science and social science disciplines.
Research Design & Statistics 2 (PSY 300)
This course will further develop students’ quantitative skills, with a specific focus on formalism (i.e., the explicit link between measurement, mathematics, and real-world phenomena) in the behavioral sciences, particularly psychology and neuroscience, as well as interdisciplinary research across the social sciences. This course will prepare students to consume, create, and critique quantitative knowledge. Course content will address: 1) theoretical & conceptual mathematics necessary for research in behavioral sciences, 2) analysis and interpretation of data in behavioral sciences, and 3) computer-aided computation. Descriptive, correlational, and experimental methods of research will be examined. Primary focus will be on data analysis (including descriptive and inferential statistics, and basic modeling), interpretation, and communication of quantitative analysis (in written and visual form).
Psychological Research: Design & Analysis (PSY 310)
Introduction to psychological research. Descriptive, correlational, and experimental methods of research will be examined. Primary focus on data analysis including descriptive statistics and inferential statistics with emphasis on analysis of variance. Mandatory weekly computer lab. Recommended in the sophomore/junior year for majors. This course is no longer offered at Davidson.
Psychological Research: Quantitative and Experimental (PSY 313)
Science has experimentation and quantification at its foundation. This course will focus on the interdependency between methodology and measurement in Psychology with an emphasis on using human-machine interfaces (e.g., virtual reality, digital interaction, social media, computational modeling, eye/body movement tracking) to enhance accuracy, replicability, and explanation. We will survey a range of research designs using a behavioral dynamics framework to answer student-driven questions about human experience. We will explore these topics via readings, discussion groups, peer-led teaching, and by conducting, reviewing, and presenting an original research project. This course will be completed as part of a small team. Students are required to have taken PSY 101 and a statistics course in any department or have the permission of the instructor.
Psychological Modeling (PSY 367)
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. This course can also be applied to Data Science or Applied Math major/minors.