Quantitative Data Analysis

Graduate-level training in turning real research data into defensible empirical claims, with as much emphasis on choosing the right tool for a given question and data structure as on running the test itself. You work in R throughout the semester, moving from exploratory analysis and hypothesis testing into the OLS regression family and then into causal inference designs (IV, RDD, DiD) and maximum likelihood models like logit and survival, with the grade resting on response papers, a paper proposal, and a term research essay rather than exams. It functions as the methods backbone for the rest of your graduate work, whatever empirical paper you eventually write, this is where you learn to actually defend the numbers in it.