НИС

QCA also known as Boolean algebra is one of the ways how to circumvent the problems of ‘the too few cases and too many variables’ and ‘the small-N problem’. QCA combines strict logic of causal inference without meeting a large number of frequentists statistics assumptions (e.g. sufficient number of cases, degrees of freedom etc.). QCA draws on deterministic logics instead of stochastic one that opens more opportunities to study complex and intertwined causal relations, mediations and moderation (the so-called ‘third variable’ problems). Thus, QCA is a mid-way between single case studies and large-N analysis.

During these modules, we will learn about methodological assumptions of QCA, applicability of the method, and rules of results’ interpretation. We will use specialized software tailored for QCA in R or R Studio.

Number of students for the block: max. 30.

This is a course about studying the power on the ground. In other words, this part of the research seminar aims to help you find your way to study politics “from below”. Ethnography – with its roots in various traditions of anthropology and sociology – implies immersion of a researcher in the world that she studies. We will try to explore how immersion can be beneficial for the study of politics/policy/international relations/development. We will see how assumptions, theories and concepts that might be guiding our research at the start are often transformed in and by the field. The reading list includes a number of interesting political ethnographies as well as more specific pieces on ethnographic methodology, method and research techniques in political science and other relevant disciplines. We will cover principal research methods: (participant) observation, fieldnote writing, and ethnographic interviewing, as well as issues of research ethics and security. We will discuss how to write ethnographies, including often problematic parts on choices of research methods and (self) reflexivity. The fieldwork that you will need to conduct (please see below) will ideally be linked to your broader research interests and will, hopefully, prepare you for a smoother independent ethnographic fieldwork in case you would like to conduct one for your BA final work.

Number of students for the block: max. 30.

With the advent of new technologies and forms of communication, the modern world began to be characterized by the creation and accumulation of large volumes of very diverse information. Most of the data is stored in text format (for example, scientific articles, posts in social networks, letters, transcripts of speeches, official documents, etc.). Computational methods for text analysis promise to aid at the scale where traditional content analysis is not feasible. This course will be dedicated to the major techniques for mining and analyzing text data to discover different patterns. We will also study some tools for data collection and visualization. The goal of the course is to provide a basic understanding of the proper usage text collections in quantitative analysis. R programming environment will be used as a toolbox for text analysis.

Number of students for the block: max. 30.