HD 6610 - Text and Networks in Social Science Research

(crosslisted) GOVT 6619 , INFO 6610 , SOC 6610  
(SBA-HE)      

Recommended prerequisite: some R or similar (e.g., python) programming experience.

W. Hobbs.

This is a course on networks and text in quantitative social science. The course will cover published research using text and social network data, focusing on health, politics, and everyday life, and it will introduce methods and approaches for incorporating high-dimensional data into familiar research designs. Students will evaluate past studies and propose original research.

Outcome 1: Learn to critically evaluate empirical research that uses text as data or social network analysis.

Outcome 2: Connect fundamentals of research design to high-dimensional data analysis.

Outcome 3: Develop verbal and written skills via in-class discussion, presentations, and written assignments.

Outcome 4: Learn to represent complex relationships quantitatively and conduct high-dimensional data analyses using statistical programming.

Outcome 5: Learn methods for avoiding over-fitting in high-dimensional data analysis.