Research Methods for Social Science & Public Health
Methods and best practices for conducting research in the social sciences and public health
Rolling Basis – Course offering for new cohort begins every 5 weeks
Live via Zoom
Research Methods for Social Science & Public Health (8-Day Weekend Program)
Duration: 8 Days (4 weekends)
Schedule: Saturday/Sunday, 3-4 PM ET (New York Time)
Registration closes: October 31, 2025
Format: Live via Zoom
Payment Options: Bank Transfer, Venmo, or Zelle accepted.
Course Fee: $250 (limited scholarships and variable pricing available based on Country of participant)
About the Training
This course provides a graduate-level introduction to research design and methodology for social science and public health research, using detailed case studies grounded in real methodological failures.
Participants begin with the foundations of quantitative research design and measurement, progress through sampling, inferential statistics, and causal reasoning, and conclude with mixed-methods integration and a full research proposal capstone.
The course emphasizes design judgment over statistical mechanics — identifying confounding, selection bias, and sampling flaws before they undermine a study — and communicating findings with appropriate rigor in academic, public health, and policy contexts.
- Instructor-led, live Zoom sessions
- Detailed, discussion-driven case studies grounded in real methodological flaws
- Quantitative design → causal inference → mixed-methods progression
- Strong focus on research design judgment & causal reasoning
- Capstone research proposal suitable for IRB or grant submission
Who Should Register
Grad students in social science, public health, policy, or related fields beginning thesis, dissertation, or capstone research; early-career researchers, program evaluators, and cross-disciplinary researchers
- Basic R and statistics familiarity
- No prior ML experience required
- Conceptual, not purely mathematical approach
Learning Objectives
- Translate a substantive research question into a testable research design appropriate to the question's causal and descriptive demands
- Distinguish experimental, quasi-experimental, and observational designs and identify the threats to validity specific to each
- Design defensible sampling strategies and justify sample size using power considerations, not convention
- Operationalize abstract constructs into measurable variables and evaluate instruments for reliability and validity
- Critically evaluate published quantitative research for confounding, selection bias, and misreported uncertainty
- Draft a methodologically sound research proposal appropriate for IRB submission, grant application, or thesis/dissertation proposal
Translate a substantive research question into a testable research design appropriate to the question's causal and descriptive demands
Build, tune, and evaluate predictive and unsupervised models.
Implement feature engineering and interpret model outputs.
Communicate findings using reproducible R Markdown or Shiny dashboards.
Design and present an end-to-end ML project.
Weekly Breakdown
Each week includes:
🎓 Lecture slides (PowerPoint)
💻 Case study
📊 Written critique or design exercise
Computing & Tools
Quantitative concepts are illustrated in R using packages like pwr, dagitty, broom, and ggplot2, though the course is designed to be software-agnostic — every method translates directly to SPSS or Stata if that’s your team’s primary tool.
All case studies, discussion materials, and R illustrations are downloadable from the course portal.