Christoph Stadtfeld: Catalogue data in Autumn Semester 2019
|Prof. Dr. Christoph Stadtfeld
Professur für Soziale Netzwerke
ETH Zürich, WEP J 16
|+41 44 632 07 93
|Humanities, Social and Political Sciences
|Behavioral Studies Colloquium
|C. Stadtfeld, U. Brandes, H.‑D. Daniel, T. Elmer, C. Hölscher, M. Kapur, H. Nax, R. Schubert, E. Stern
|This colloquium is about 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 features invited presentations from internal and external researchers as well as presentations of doctoral students close to submitting their dissertation research plan.
|Participants are informed about recent and ongoing research in the field. Presenting doctoral students obtain feedback on their dissertation research plan.
|The covers the broadly understood field of behavioral science, including theoretical as well as empirical research in Social Psychology and 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.
|Prerequisites / Notice
|Doctoral students in D-GESS can obtain 2 credits for presenting their dissertation research plan.
|Open Debates in Social Network Research
Number of participants limited to 30
|C. Stadtfeld, T. Elmer, A. Vörös
|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.
|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.
- 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
|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.
Particularly suitable for students of D-INFK
Students are required to have basic knowledge in inferential statistics, such as regression models.
|C. Stadtfeld, V. Amati
|Network Science is a distinct domain of data science that focuses on relational systems. Various models have been proposed to describe structures and dynamics of networks. Statistical and numerical methods have been developed to fit these models to empirical data. Emphasis is placed on the statistical analysis of (social) systems and their connection to social theories and data sources.
|Students will be able to develop hypotheses that relate to the structures and dynamics of (social) networks, and tests those by applying advanced statistical network methods such as stochastic actor-oriented models (SAOMs) and exponential random graph models (ERGMs). Students will be able to explain and compare various network models, and develop an understanding how those can be fit to empirical data. This will enable them to independently address research questions from various social science fields.
|Prerequisites / Notice
|Students are required to have basic knowledge in inferential statistics and should be familiar with linear and logistic regression models.