Course descriptions

The Social Sciences Directorate of Doctoral Studies (SPL 40) has developed a list of courses that will be offered on a regular basis (at least once per year). In response to the call for suggestions of courses that is circulated in the autumn of the year before, teachers can select courses from the following list or suggest additional topics of choice.

These templates are open for adaptation and, depending on the expertise and interest of the course leader and the students, a focus can be set on specific aspects. An overview of the subject should still be provided.

Methods seminars

Ethnography

Ethnographic methods entail varying forms of data collection (through participant observation, interviews, archival work, as well as collecting objects and visual material). They are deployed to capture and analyse social practices, relations, experiences and their transformations in their social, political and economic embedding with a special attention to the construction and mediation of their meanings. Experience and practice and their contextualization have epistemological primacy in ethnographic research. Personal narratives and the contextualized enactments of everyday life provide the irreplaceable entry into the analysis of social practices, and (re)production of meanings (including the embodied and tacit knowledge) in ethnographic research. They establish the basis for the generation of ethnographic knowledge and theoretical insights.
This seminar addresses the processual nature of fieldwork, from field-access, to research ethics, positionality and the deployment of various methods, and aims to explore with the students the methodological and ethical challenges of ethnographic research in an increasingly interconnected and mediated world. We will also focus on different techniques and strategies of taking field notes, analysing, presenting and writing up data. The interests and research projects of the participants establish the ground upon which different approaches to ethnographic research will be discussed.

Interpretative methodologies

In this course, knowledge on epistemological groundings of interpretive methodologies, different methods of data elicitation and analysis and different ways of generalizing from empirical data to reach theoretical concepts is offered and discussed.
The assumption that the social world is based on communication and social interaction in different modes and media as well as the principle of openness for generating new knowledge by abduction are at the core of the epistemological groundings of these research programs.  
Based on that, different approaches with different research designs have developed, as e.g. Grounded Theory, Objective or Social Scientific Hermeneutics, Narrative Inquiry, Documentary Method, Discourse Analysis. In the course, the rational of each of these approaches and the respective kind of knowledge that can be gained by them is presented. Accordingly, different methods of data elicitation (as e.g. lifeworld ethnography, narrative interview, problem-centred interview with a narrative focus, group discussions, compiling a corpus of texts/images) as well as different procedures of data analysis (e.g. coding along the logics of Grounded Theory, hermeneutic/reconstructive text and image analysis, discourse analysis) are addressed.
Finally, different ways of generalizing from empirical findings as to develop theoretical concepts are presented and discussed.  
Depending on the interests of participants and the expertise of the course leader, knowledge on a specific approach and method can be deepened.

Qualitative content analysis

Qualitative content analysis is a semi-structured systematic approach that comprises a bundle of research techniques. It allows for categorizing a variety of textual data (interview transcripts, speeches, observational protocols, political documents, media texts, etc.) with the aim to extract meaning from the content of the data. Codes are either inductively derived directly from the text or deductively from the theory/the state of research. The descriptive analysis of manifest content results in creating categories, while the interpretation of latent content and underlying context leads to the elaboration of core themes.
The course provides insights into the epistemological groundings of qualitative content analysis and introduces different approaches to data elicitation and analysis. It presents types of semi-structured interviews (mainly problem centred interview and expert interview), focus groups, open questions in questionnaires, and ways of capturing social media data. Theory-driven/deductive and data-driven/inductive coding are discussed, with a focus on both descriptive and interpretive aspects. Supportive software such as MAXQDA and Atlas.ti is also introduced. Finally, strategies and framework conditions of generalizing qualitative research findings are highlighted.
Selected aspects of qualitative content analysis can be deepened, according to participant’s interests. Students are encouraged to present their own data, which are then used for in-class group work.

Introduction to research design from a quantitative perspective

The seminar focuses on various questions with regards to a PhD research design in the social sciences. We deal with the following questions: which elements should be included in a PhD research proposal in the social sciences? How to find a ‘good’ (overarching) research question? How to embed the research question theoretically? Furthermore, we deal with measuring concepts in the social sciences and causal inference. By doing so, PhD students should be able to find the best methodological approach to tackle their research question. The aim is to develop a first draft of a PhD research proposal. Students will have ample opportunities to present parts of their PhD research proposal.

Introduction to regression models

This course provides a practical and applied introduction to ordinary least squares (OLS) regression models, one of the most widely-used statistical methods in the social sciences. By the end of this course, you will be able to construct and interpret OLS regression models. You will have a firm understanding of the assumptions of the model, the differences between various types of independent variables and how to identify and address possible dangers and problems. You will also be able to evaluate critically OLS models used in scholarly journals. We will begin by reviewing basic statistical concepts, such as comparing means and testing hypotheses, before moving on to the analysis of the association of two continuous variables. We then discuss simple linear regression and the assumptions underlying OLS regression. The final sessions cover the core of this method. First, we examine in detail multiple regression models, concentrating on the practical interpretation of results. Then, different types of explanatory variables are introduced, with a focus on binary/nominal variables and interaction effects. Finally, an overview of possible problems and their remedies is provided, and we will consider how to approach model-building in OLS regression.

Advanced level courses (seminars or research workshops)

Ethnographic writing and theory building

Compared to other forms of empirical research in the social sciences, ethnography appears as a relatively unstructured style of research, which might be one reason for the difficulty many students experience in developing their interpretation and theoretical arguments. The course is geared towards students who already gathered their ethnographic data and are in the stage of writing up. We will work with this material and discuss different distancing and interpretative strategies that can be helpful in the course of interpretation. Together we will try to develop innovative arguments brought-in by participants in the writing up phase in relation to selected ethnographic studies.

Analyses following interpretative methodologies

Theory building in interpretive approaches is based on extended processes of interpreting and analysing data of different kinds with different methodological and methodical approaches. According to standards of interpretive research, this kind of analysis is expected to be carried out in groups as to ensure the intersubjective validity and quality of results. The main purpose of the course is to guide group interpretation of empirical material, which has been gathered in each project according to the respective methods applied following an interpretive research approach and design. This will enable participants to stay on track with their analysis on the one hand side and to develop reasonable combinations of different methods without getting confused on the other. In each session particular steps of analysis of an ongoing project will be practised together and open questions and solutions for unforeseen problems discussed. Thus, all participants will be able to deepen their understanding of different methods and approaches (following e.g. Grounded Theory, Hermeneutic Text and Image Interpretation, Discourse Analysis), which will enable them to carve out their own particular way of analysis. In addition, they will be motivated to build matching interpretation groups among each other as to be able to work continuously on their material and to profit from the knowledge of others.

Advanced qualitative content analyses (focus on multimodal data from social media)

The research laboratory is aimed at students who have already collected (at least part of their data) and want to process these data (or parts of it) using qualitative content analysis. The course provides guidance in terms of coding, categorizing, and elaborating core themes. Apart from the input and feedback from the lecturer, the research laboratory also relies on peer learning. Hence, students will work on their own material in small teams and support one another in analysing and interpreting relevant data. Depending on the interests of the course participants, the expertise of the course leader and the type of data collected, a focus can be placed on qualitative multimodal analyses of social media data or other fields.

Advanced research design from a quantitative perspective and causal inference

This course provides an overview of different advanced quantitative methods that are used in the social sciences to draw inferences about causal relationships from large-N observational data. We first introduce Neyman and Rubin’s “potential outcomes framework” of causality that forms a theoretical basis for the course and then survey different classes of popular methods for causal inference. These may include: matching method, instrumental variables, difference-in-differences, synthetic control, and regression discontinuity designs. These methods claim to advance on standard regression models by adjusting for selection bias on observables and unobservables. With regard to each method covered, we will address its theoretical foundations and assumptions, practical considerations and challenges, critical discussions of applications, implementation in software as well as interpretation of results.

Advanced regression models

This course covers regression models for categorical data. We start by revisiting the linear model and evaluate the linear probability model (LPM) as a first approach to analysing categorical data. We discuss different estimation techniques and the fundamentals of maximum likelihood estimation (MLE) will be introduced. Next, we explore a broad range of generalised linear models (GLMs) including models for binary, multinomial and ordered outcome variables. We learn how to interpret and visualise their model results by deriving quantities of interest. Finally, we will cover further extensions of these models, such as hierarchical/multilevel models and models for panel data.

Advanced quantitative text analysis

Facing the massive volumes of text data that are available in digital format and valuing their potential, over recent years social scientists have increasingly turned to methods that rely on the support of computer power, so-called computer-assisted or automated text analysis methods. The text-as-data methods are used to draw reproducible and valid inferences or meanings from documents. As an enhancement of the more classical manual methods of content analysis, automated methods of text analysis are becoming prevalent in disciplines that are overall increasingly computationally oriented. This course is aimed at people with some knowledge of automated text analysis who want to use this method in their PhD and/or want to deepen their expertise of the matter. The course covers topics related to data collection, data processing, quality control, and the critical interpretation of results.