The goal of this course is to provide an accessible entry into the world of R which prepares the participants to confidently approach the most common analysis tasks using R. This course is particularly recommended for those who want to take a methods course follow-up that relies on R.
Python is the most popular programming language of data science, used in natural language processing, machine learning, and artificial intelligence. This five-day Python programming course is designed for social scientists - with zero experience in programming - who would like to conduct data collection, analysis, and modelling with Python.
Concepts are fundamental building blocks of social science research. However, they are also contested, difficult to construct, and subject to change. This course provides participants with an introduction to the role of concepts in theories, methods, and research design.
This course teaches early career researchers how to combine two or more different methodological approaches in their empirical projects. We will cover traditional and novel approaches to multi-method research (MMR)—which integrate multiple methods and data in a single article or dissertation/book.
This course introduces approaches from narrative methods to discourse analysis and the typical steps of the research process -from how to formulate interpretive research questions to how to present and document them. It provides students with an introduction to different interpretive methods.
The course covers foundational topics of qualitative data analysis and trains participants, hands-on, to perform two popular data analytic techniques: thematic analysis and qualitative content analysis.
This course is for anyone interested in analysing how language is used (and abused) in different socio-political contexts. You will learn how to analyse texts, how to interpret your findings and how to build a CDS-oriented research project.
Examining and understanding human behavior, including how social, organizational, administrative and political processes play out in different arenas, is a key concern for many social scientists. Ethnographic approaches are well suited to shed light on a broad variety of political and organizational phenomena; yet, they are still rarely used in political science research.
Qualitative Comparative Analysis models causal complexity by analyzing necessary or sufficient conditions for an outcome. This course introduces both the nuts and bolts of QCA, and the most advanced analytic tools available in the R software environment needed for a publishable QCA.
Process tracing is a case-based method for learning about how things work within cases. This two-week course provides a practical, hands-on introduction to process tracing. In the course, we will work with published examples and your own project.
This course offers guidance to scholars exploring macro-historical questions and looking to Comparative Historical Analysis to better understand the methodological implications of incorporating historical into social inquiry.
The purpose of this course is to help you “unlearn” some of the default lessons about the conduct of social-scientific inquiry. This field is namely dominated by neopostivism and substantialism, but there are other approaches of inquiry which you'll learn here.
This course aims to sharpen R skills and provide materials for R users who are keen to learn R beyond the basics. We will cover how to write and work efficiently, build better functions, create R packages and use version control (git).
Experiments became a standard part of the methodological toolkit of social scientists, they are well-suited to facilitate true casual inferences. The course covers implementing, analyzing, and critically evaluating such designs.
Social scientists are often confronted with outcome variables that are not linear, such as binary, or ordinal survey items, or event counts. The aim of this course is to make students comfortable with applying GLM regression techniques.
Social scientists have never been more relevant than they are today - for the present and the future. With the increasing availability of data, the fast paced professionalization of research methods, and the societal problems around us, social scientists are uniquely positioned and trained to contribute to positive social impact.
Discourse Network Analysis is a methodological toolbox for measuring and analyzing policy debates and their development over time. At its core, the software Discourse Network Analyzer (DNA) allows researchers to manually code actors’ opinions about policies in text data.
In this course, we will move beyond well accepted qualitative case study design templates (namely positivistic, interpretivist and critical realist templates) to develop and use a ‘bricolage’ approach for your case study research.
Decision-making is often complex: interests of those involved can conflict and several options often compete for support and funding. In addition, decision-making needs to be sensitive for underlying motives, belief systems, and personal and political agendas.
What happens after fieldwork? How do we get from data generated using ethnographic methods to written accounts that are not only academically robust, but also meaningful? In this course, we will explore the deskwork and textwork phases of using ethnographic/interpretivist methods, considering the issues and challenges involved in taking research projects from the field to the page.
This course guides researchers in situating and implementing their own qualitative data work within comparable case and process tracing strategies as two powerful research designs increasingly used across social sciences.