Christoph Stadtfeld: Catalogue data in Spring Semester 2023 |
| Name | Prof. Dr. Christoph Stadtfeld |
| Field | Social Networks |
| Address | Professur für Soziale Netzwerke ETH Zürich, WEP J 16 Weinbergstr.109 8006 Zürich SWITZERLAND |
| Telephone | +41 44 632 07 93 |
| christoph.stadtfeld@ethz.ch | |
| URL | http://www.social-networks.ethz.ch/ |
| Department | Humanities, Social and Political Sciences |
| Relationship | Associate Professor |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 851-0252-04L | Behavioral Studies Colloquium  | 0 credits | 2K | C. Hölscher, U. Brandes, D. Helbing, M. Kapur, C. Stadtfeld, E. Stern, V. Zimmermann | |
| Abstract | This colloquium offers an opportunity to discuss recent and ongoing research and scientific ideas in the behavioral sciences, both at the micro- and macro-levels of cognitive, behavioral and social science. The colloquium features invited presentations from internal and external researchers as well as presentations of doctoral students close to submitting their dissertation research plan. | ||||
| Learning objective | Participants are informed about recent and ongoing research in different branches of the behavioral sciences. Presenting doctoral students obtain feedback on their dissertation research plan. | ||||
| Content | This colloquium offers an opportunity to discuss recent and ongoing research and scientific ideas in the behavioral sciences, both at the micro- and macro-levels of cognitive, behavioral and social science. It covers a broad range of areas, including theoretical as well as empirical research in social psychology, research on higher education, sociology, modeling and simulation in sociology, decision theory and behavioral game theory, economics, research on learning and instruction, cognitive psychology and cognitive science. The colloquium features invited presentations from internal and external researchers as well as presentations of doctoral students close to submitting their dissertation research plan. | ||||
| Prerequisites / Notice | Doctoral students in D-GESS can obtain 1 credit point for presenting their research in the colloquium. | ||||
| 851-0252-06L | Introduction to Social Networks: Theory, Methods and Applications This course is intended for students interested in data analysis and with basic knowledge of inferential statistics. | 3 credits | 2G | C. Stadtfeld, T. Elmer, X. Xu | |
| Abstract | Humans are connected by various social relations. When aggregated, we speak of social networks. This course discusses how social networks are structured, how they change over time and how they affect the individuals that they connect. It integrates social theory with practical knowledge of cutting-edge statistical methods and applications from a number of scientific disciplines. | ||||
| Learning objective | The aim is to enable students to contribute to social networks research and to be discriminating consumers of modern literature on social networks. Students will acquire a thorough understanding of social networks theory (1), practical skills in cutting-edge statistical methods (2) and their applications in a number of scientific fields (3). In particular, at the end of the course students will - Know the fundamental theories in social networks research (1) - Understand core concepts of social networks and their relevance in different contexts (1, 3) - Be able to describe and visualize networks data in the R environment (2) - Understand differences regarding analysis and collection of network data and other type of survey data (2) - Know state-of-the-art inferential statistical methods and how they are used in R (2) - Be familiar with the core empirical studies in social networks research (2, 3) - Know how network methods can be employed in a variety of scientific disciplines (3) | ||||
| 851-0252-07L | Humans and Social Networks in the Digital Age  | 3 credits | 2S | C. Stadtfeld, A. Espinosa Rada | |
| Abstract | Social network research develops through contributions from many scientific disciplines. Among others, scholars of sociology, psychology, political science, computer science, physics, mathematics, and statistics have advanced theories and methods in this field - promoting multiple perspectives on important problems. We will put acclaimed (network) theories into perspective with current research. | ||||
| Learning objective | Research on social networks has developed as a highly interdisciplinary field. By the end of this seminar, students will be able to identify and compare different discipline- and subject-specific approaches to social network research (coming mostly from sociology and psychology). They will be familiar with recent publications in the field of social networks and be able to critically participate in a number of open debates in the field. Among others, these debates are centered around the types and measurement of social relations across different contexts, the importance of simple generative processes in shaping network structure, the role of social selection and influence mechanisms in promoting segregation and polarization. Learning Objectives: - Know the most relevant social network terminology and concepts - Know the most relevant sociological and psychological social network theories - Be able to develop meaningful social networks research questions - Be able to design your own social networks study - Critically examine empirical social networks research | ||||
| Content | Social network research develops through contributions from many scientific disciplines. Among others, scholars of sociology, psychology, political science, computer science, physics, mathematics, and statistics have advanced theories and methods in this field - promoting multiple perspectives on important problems. We will critically examine acclaimed (network) theories of sociology and psychology and put them into perspective with current research. This course aims to present and structure open debates in social network research with a focus on social network processes, individual outcomes, and emergent phenomena. | ||||