For Harvard students: Lecture: Mondays 2:00-4:00 (location TBD); Section: Wednesdays 6:00-7:00 and 7:00-8:00 (CGIS-K354). For others: register here for online access (for credit or auditing).
Phone: 617-500-7570, Administrative Assistant: 617-495-9271
Office: 1737 Cambridge Street (CGIS), K313
For students who've taken a course in linear regression (such as Harvard's Gov2000), this course gives you the tools to learn new statistical methods or build them yourself. We focus on practical methods useful for real social science research. We aim to give you two types of skills.
First, we who how to develop new approaches to research methods, data analysis, and statistical theory. More advanced statistical theory is not required when data and variables follow standard assumptions. Since this is not usually the case in most of the social sciences, we often cannot use ready-made statistical procedures developed elsewhere and for other purposes. We teach the underlying theory of inference (which, at its most fundamental is merely using facts you know to learn about facts you don't know); once understood, we can easily “reinvent” known statistical solutions to accommodate social science data, or even invent original approaches when required. Students will learn how to read an original scholarly article describing a new statistical technique, implement it in computer code, estimate the model with relevant data, understand and interpret the results, and present and explain the results to someone unfamiliar with statistics.
Second, students will learn how to make novel substantive contributions to the scholarly literature. A substantial portion of those who complete the course publish a revised version of their class paper in a scholarly journal. For most students, this is their first professional publication. For papers from previous years, see the Gov 2001 Dataverse.
Most of the course will be lecture-based, and several more collaborative approaches will be used too. For resources and tools, see the section Learning Resources.