201 - 210 of 219 results for: SOC

In this course, we learn how to collect and analyze social network data. We begin by learning the fundamentals of graph theory and replicating well-known network studies. In the process, we cover classic network methods from centrality to block-modeling. We then move to the frontiers of network analysis. Topics include visualization, modeling and simulation, dynamic network analysis, network experiments, semantic network analysis, and analyzing social networks at scale. Sources and ways of collecting network data will be discussed and students will apply methods they learn to data of their own.

Priority to Sociology doctoral students. Emphasis is on observational and interview-based research. Limited enrollment.

Presentations and discussion of ongoing ethnographic, interview-based, and other fieldwork research by faculty and students . May be repeated for credit. Prerequisite: Sociology doctoral student or consent of instructor.

Enrollment limited to first-year Sociology doctoral students. Other students by instructor permission only. This course provides a conceptual and applied introduction to quantitative social sciences methodology, including epistemology, measurement, sampling, descriptive statistics, statistical inference, and ordinary least squares regression. Students will be introduced to both the methodological logic and techniques of statistical data analysis, including application in the software Stata. The course will situate quantitative methodology within the broader frame of sociological research. It will present the purpose, goals, and assumptions behind techniques of statistical analysis and will discuss applications to analyzing data and interpreting results. In addition to the lecture time, SOC 381 includes a weekly section for further practice with statistical software and application.

Enrollment limited to first-year Sociology doctoral students. Other students by instructor permission only. Required for Ph.D. in Sociology. Rigorous treatment of linear regression models, model assumptions, and various remedies for when these assumptions are violated. Introduction to panel data analysis. Enrollment limited to 15. Prerequisites: 381.

Required for Ph.D. in Sociology; other students by instructor permission only. enrollment limited to first-year Sociology doctoral students. The rationale for and interpretation of static and dynamic models for the analysis of discrete variables. Prerequisites: 381 and 382, or equivalents.

Social science researchers often deal with complex data and research questions that traditional statistics models like linear regression cannot adequately address. This course offers the opportunity to understand and apply two widely used types of advanced regression analysis that allow the examination of 1) multilevel data structures (multilevel models) and 2) multivariate research questions (structural equation models).

Workshop on research methods and writing research papers for second year Sociology doctoral students. Ongoing student research, methodological problems, writing challenges, and possible solutions. Required for second year paper.

Workshop on research methods and writing research papers for second year Sociology doctoral students. Ongoing student research, methodological problems, writing challenges, and possible solutions. Required for second year paper.