Program-Wide Required Courses and Credits
All students in the PhD Program in Population Health Sciences (regardless of field of study) are expected to complete the following requirements:
- Quantitative Research Methods in Population Health Sciences PHS 2000, a year-long course to be taken in the first year. This course forms the core of the PhD coursework in research methods. Methods from different disciplines with relevance to all five fields of study are included (see below for details). (10 Chan credits/ 8 FAS credits)
- Introduction to Epidemiology EPI201 and Elements of Epidemiological Research EPI202 to be taken in the first year. This sequence equips all students with understanding of basic research concepts, causal theory, epidemiology, and study design. (5 Chan credits/ 4 FAS credits)
- Introduction to Public Health number TBD, providing an introduction to the social and scientific context, content, and implications of theories of disease distribution, past and present. (2.5 Chan credits/ 2 FAS credits)
- Responsible Conduct of Research HPM 548, introducing basic ethical and regulatory requirements for conducting bench, animal, clinical, and public health research. (1.25 credits)
Quantitative Research Methods
PHS 2000: This is the core year-long quantitative methods course for the Population Health Science PhD students at the School of Public Health. The course integrates methods and concepts from the various disciplines represented by population health sciences to equip students with the methodological tools to conduct their own research as well as collaborate across fields of study and areas of specialization. The course will cover foundational statistical methods including linear and logistic regression, generalized linear models, survival analysis, longitudinal data analysis, and multilevel modeling. Discussion will be given to important concepts including study design, sampling, scientific inference, causal reasoning, measurement, and replication. The course will also provide an overview of a number of additional and sometimes more advanced methods including Bayesian statistics, big data methods, missing data, sensitivity analysis, propensity scores, time-varying exposures, interaction, mediation, instrumental variables, regression discontinuity designs, difference-in-difference methods, selection models, time series, bootstrapping, simulations, and meta-analysis. With these latter topics, emphasis will be placed on understanding the basic definitions, assumptions, and methodology. Students will be referred to further readings and courses to gain more detailed understanding. Various software resources will be used throughout the course. The course will prepare students to critically read through the empirical population health science literature, and to implement a number of different methods in their own research.
Prerequisites: This course is restricted to first-year PhD students in Population Health Sciences.