BA Liberal Arts and Sciences, 7.5 ECTS (responsibilities: coordination, lectures, tutorials)
Description: This course is an introduction to sociology. It treats sociology as a science and a profession, emphasizing scientific questions, theories, methods, findings, and their applications. It covers a wide range of topics and social phenomena, such as inequality, crime, immigration and ethnicity, intergroup hate, misperceptions, polarization, religion, gender, and modernization. During the course, students are introduced to a useful set of sociological ‘tools’ and ‘principles’, which helps them to think like a sociologist and to describe and understand social phenomena in a scientific way. Furthermore, students get an introduction to key sociological concepts, theories, perspectives, methods, and stylized findings. We meet two times a week in lectures and tutorials. During lectures, students discuss in small groups their answers to the assignment questions. Group discussions are followed by plenary sessions, during which students’ answers to assignment questions are discussed more broadly. Occasionally, the lecturer reviews and explains the most important concepts and terms. In the tutorial meetings, we go beyond the textbook materials and take an in-depth view on some research. Two research papers are presented by students in each meeting.
BA Sociology, 7.5 ECTS (responsibilities: course development, coordination, lectures, tutorials, computer labs)
Description: The course introduces some of the main methods of empirical measurement and statistical modeling that are used in quantitative sociological research, such as reliability analysis, factor analysis, and advanced methods of multiple regression analysis. These methods are used to address substantial research questions. The first week is a recap of multiple regression and measurement basics. The following six weeks cover measurement and statistical modeling issues related to trust, reputation and social networks by means of survey data (e.g., General Social Survey), online process data (e.g., transaction data from peer-to-peer online markets) and social network data (e.g., complete class networks). Students meet three times a week in lectures, tutorials and computer lab sessions. In the lectures, the methodological and statistical theories behind measurement and modeling issues in social data analysis are discussed. In the tutorials, the weekly assignments are introduced and the results of previous assignments are discussed. In the computer lab sessions, students apply the research skills and statistical theory to real life examples and work on their assignments and research essays.
MA Sociology and Social Research, 7.5 ECTS (responsibilities: lectures, tutorials)
Description: In this course, problem-driven and systematic (deductive) theory construction, model building, and explanation in social science are illustrated using, among others, macro- and micro-features of explanatory models as well as macro-micro-macro transitions. Basic micro-models include an introduction to principles of rational choice theory, game theory, behavioral models and applications of theoretical tools in sociology (these models are used in many fields of sociology, including fields such as the interface of stratification and households or social networks and social capital that receive systematic attention in other courses). Applications focus on key problems of sociology (cohesion and inequality), with an emphasis on paradigmatic macro-phenomena related to these problems such as effects of networks and institutional arrangements (organizations) on cohesion and inequality, and, conversely, effects of cohesion and inequality on the dynamics of networks and institutional arrangements. Generating testable hypotheses on macro-phenomena from underlying theory and the ability to assess available literature with respect to the 'performance' of such hypotheses in empirical research is a topic of the course. The course features a mix of lectures, assignments, presentations and class discussions.
MA Applied Data Science, 7.5 ECTS (responsibilities: lectures, computer labs)
Description: The course is divided in two parts. The first part of the course is taught by colleagues form the Department of Psychology and focuses on image processing and the human visual system, viewing the visual system as a deep network. The second part introduces important concepts and challenges in social network analysis and modelling, which simulate interactions between humans. We first go through the basic concepts of social networks and their measures such as centrality, coreness, clustering and path length. We then study theories and models that explain the formation of social networks. Next we study contagion processes within networks. Our focus is on theories of simple and complex contagion and their explanation in the spread of diseases and behaviors. In the last week we discuss how we can affect diffusion processes based on the advances in diffusion models and influence prediction. The course comprises of weekly lectures and computer lab sessions in which students work on assignments covering models of social networks using R or Python.
BA Sociology, 7.5 ECTS (responsibilities: course development, coordination, lectures, tutorials)
Description: The course starts by outlining the environmental crises that we are facing, conceiving them as social problems and locating the role of sociology in general and of sociological concepts and theories in particular. In weeks 2 through 4 the course covers relevant topics in political sociology such as revolutions and state formation (week 2), political participation (week 3), and the role of the state in shaping socio-economic processes (week 4). In week 5 the course focuses on the causes and consequences of environmental impacts and makes clear how different stakeholders shape the debates about environmental crises. The last part of the course (weeks 6 through 8), integrates both topics by thematizing social environmental movements (week 6), discussing the potentials and pitfalls of alternative forms of governance and economic systems (week 7), and showcasing the application of environmental sociology in the context of the sustainability transition through a guest lecture (week 8). A selection of topics covered by the weekly lectures is then deepened in assignments and group work in the weekly tutorials.