While data analysis can be challenging, applying data analysis methods to empirical social science studies adds a layer of complexity. This course serves three main purposes:
To facilitate understanding of basic quantitative and qualitative empirical analysis methods.
To equip students with the skills needed to conduct analysis both manually and using software packages.
To impart knowledge on how to apply these analysis methods for answering research questions from a research design perspective, and how to assess the quality of an empirical study.
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”).