While data analysis can be challenging, applying data analysis methods to empirical social science studies adds a layer of complexity. This course has three major focuses:
- A set of statistical concepts and measures that are core to evidence-based policy analysis. Understand when and how to use them, and how to interpret them. (~50% efforts)
- Process of qualitative empirical policy research. Know the typical stages and have a hands-on experience. (~30% efforts)
- A customized learning module that helps you build your skills for future career. (~20% efforts)
Primary practice/software tools:
- Guided statistical calculation on paper by hand: “Paper Practice”
- MS-Excel: “In Class Practice”
- DataCamp and Brilliant: “Customized Learning”
Given the diverse backgrounds of the students, expectations will differ accordingly:
- For all students, it is expected that they will (a) gain an understanding of a set of core empirical methods and their applications in policy research, and (b) engage in customized self-development over the semester.
- For students with minimal or no statistical training, approximately 9 hours of work is expected each week: (a) 3 hours in class, (b) 1 hour in a lab or self-guided session on Excel/Python/R, (c) 3 hours outside of class for assignments (including final tests and “customized learning”), and (d) 2 hours of preparation for upcoming sessions (including readings and “paper practice”).
- For students with advanced knowledge of statistics, approximately 7 hours of work is expected each week: (a) 3 hours in class, (b) 3 hours outside of class for assignments (mostly on “customized learning” and a qualitative group project), and (c) 1 hour of preparation for upcoming sessions (including readings and “paper practice”).