Courses

Ethnographic methods involve various forms of data collection, such as participant observation, interviews, archival research and collecting objects and visual material. These methods are used to capture and analyse social practices, relations and experiences, as well as their transformations, in their social, political and economic contexts, paying special attention to the construction and mediation of their meanings. Experience, practice and contextualisation have epistemological primacy in ethnographic research. Personal narratives and the contextualised enactment of everyday life provide an invaluable entry point for analysing social practices and the (re)production of meanings (including embodied and tacit knowledge) in ethnographic research. These narratives and practices establish the basis for generating ethnographic knowledge and theoretical insights.

This seminar addresses the processual nature of fieldwork, from accessing the field to research ethics, positionality, and the deployment of various methods. It aims to explore the methodological and ethical challenges of ethnographic research in an increasingly interconnected and mediated world with the students. We will also focus on different techniques and strategies for taking field notes, analysing data, and writing reports. Participants’ interests and research projects will inform discussions on different approaches to ethnographic research.

This course offers and discusses knowledge on the epistemological groundings of interpretive methodologies, the different methods of data elicitation and analysis, and the different ways of generalising from empirical data to reach theoretical concepts. The epistemological groundings of these research programmes are based on the assumption that the social world is based on communication and social interaction in various modes and media, as well as on the principle of openness to generating new knowledge through abduction. 

Based on this, various approaches with different research designs have emerged, such as Grounded Theory, Objective or Social Scientific Hermeneutics, Narrative Inquiry, the Documentary Method and Discourse Analysis. The rationale of each of these approaches and the type of knowledge that can be gained through them is presented in the course. The course also addresses different methods of data elicitation, such as narrative interviews, problem-centred interviews with a narrative focus, group discussions and compiling a corpus of texts and images, as well as different procedures of data analysis, such as coding along the logic of Grounded Theory, hermeneutic/reconstructive text and image analysis and discourse analysis. Finally, the course presents and discusses different ways of generalising from empirical findings to develop theoretical concepts. Depending on participants’ interests and the course leader’s expertise, knowledge of a specific approach and method can be deepened.

Qualitative content analysis is a semi-structured, systematic approach comprising a range of research techniques. It enables the categorisation of various textual and visual data, such as interview transcripts, speeches, observational protocols, political documents, media texts and images, with the aim of extracting meaning from the content of the data. Codes can be derived either inductively from the text itself, or deductively from theory or the state of research. Descriptive analysis of manifest content results in the creation of categories, while interpretation of latent content and underlying context leads to elaboration of core themes.

This course provides an insight into the epistemological foundations of qualitative content analysis, introducing different approaches to data elicitation and analysis. It covers types of semi-structured interviews (primarily problem-centred and expert interviews), focus groups, open questions in questionnaires and methods of capturing social media data. Both theory-driven/deductive and data-driven/inductive coding are discussed, with a focus on descriptive and interpretive aspects. Supportive software such as MAXQDA and Atlas.ti is also introduced. Finally, the strategies and framework conditions for generalising qualitative research findings are highlighted. Selected aspects of qualitative content analysis can be explored in more depth according to participants’ interests. Students are encouraged to present their own data, which is then used for in-class group work.

This course teaches students how to design, conduct, and analyse rigorous quantitative research, from conception to dissemination. It emphasises the logic of causal inference and the alignment of research questions, hypotheses, and designs with appropriate data, measurement, and analytical strategies. Students learn to formulate empirically testable hypotheses; distinguish correlation from causation; and plan sampling, measurement, and instrumentation to maximise validity and reliability. The course also covers research ethics and open science practices, including preregistration and transparent reporting. It examines common methodological challenges such as p-hacking, the file-drawer problem, and issues of statistical power, along with potential solutions. Throughout the course, students will critique published work, develop and present their own research designs, and receive feedback from peers and the instructor. The ultimate goal is to equip students to conduct their own research projects, such as their PhD theses, and to evaluate scientific claims based on the quality of the underlying research design. 

This course introduces a broad range of advanced regression methods that are widely used in the social sciences. First, the course revisits and extends the linear model to cover categorical outcomes using the linear probability model (LPM). Building on this foundation, alternative estimation techniques and maximum likelihood estimation (MLE) are discussed. The course then moves on to explore generalised linear models (GLMs) for binary, multinomial, ordered, and count data. There is a particular focus on interpreting and visualising results using modern post-estimation techniques. In this context, the course also covers simulation-based and resampling approaches for small-sample inference. Additional topics include robust and clustered standard errors, fixed- versus random-effects specifications, hierarchical and multilevel models, and panel data methods. Practical exercises will help participants apply these methods to their own research questions.

Compared to other forms of empirical research in the social sciences, ethnography appears to be a relatively unstructured style of research. This may explain why many students find it difficult to develop their interpretations and theoretical arguments. This advanced methods course is intended for students who have already gathered their ethnographic data and are at the stage of writing it up. Together, we will work with this material and discuss various distancing and interpretative strategies that could be useful when developing a theoretical argument. Together, we will try to develop the innovative perspectives of participants in the writing-up phase in relation to selected ethnographic studies.

This advanced methods course is intended for students who have already collected some of their data and wish to analyse it using qualitative content analysis. The course provides guidance on coding, categorising and elaborating on core themes. As well as input and feedback from the lecturer, the research laboratory relies on peer learning. Students will therefore work on their own material in small teams, supporting one another in analysing and interpreting relevant data.

This advanced methods seminar provides an overview of the advanced quantitative methods that are used in the social sciences to draw inferences about causal relationships from large-N observational data. The course introduces Neyman and Rubin’s “potential outcomes framework” of causality, on which the course is theoretically based. It then covers various classes of popular methods of causal inference using observational data, such as matching methods, instrumental variable, difference-in-differences, synthetic control and regression discontinuity designs. (The list may change from year to year.) These methods use different identifying assumptions to correct for selection bias on observables and unobservables that impede causal inference. For each method covered, the course addresses its theoretical foundations and assumptions, practical considerations and challenges, critical discussions of applications, implementation in software as well as interpretation of results.

Although this is an advanced course on quantitative methods, no prior knowledge of causal inference methods is required. However, you should have a solid understanding of the fundamentals of quantitative methods, particularly OLS regression analysis.

This advanced methods seminar aims to equip doctoral researchers in the social sciences with the skills to design, analyse, and interpret rigorous experiments that yield meaningful insights into complex social phenomena. With a focus on randomised controlled trials (RCTs) in various forms, such as lab, survey, field, and online experiments, the course will  explore experimentation, considering its advantages and limitations relative to other methodologies. Throughout the semester, students will master the key elements of experimental design and analysis, including covariate adjustment, moderation and mediation, generalisability, handling noncompliance and attrition, working with panel and conjoint experimental designs, addressing ethical considerations, and applying common open science practices.