Behaviour, Psychology and Sociology

Take a look at our summer courses within the discipline of Behaviour, Psychology and Sociology.


  • INSIGHTS: Integrating Sex and Gender in Health Sciences. (RSS3.08)

    Discover the pivotal role of sex and gender in healthcare through our INSIGHTS course. This program combines expert knowledge and hands-on learning to foster a comprehensive understanding, essential for today's healthcare professionals.

  • Introduction to Data Science with R and Rstudio (RSS3.01)

    The aim of the course 'Data Science with R and Rstudio' is to learn and experience working with the software through explanations and exercises which will extend in several relevant areas of working with R and Rstudio for statistics.

  • Complexity Methods for Behavioural Science: A toolbox for studying change (RSS4.03)

    This course discusses techniques that enable the study of human behavior from the perspective of complexity science, particularly complex adaptive systems.

  • Discourse Network Analysis (RSS1.04)

    This one-week summer course will introduce discourse network analysis, a methodology that combines qualitative content analysis with social network analysis to measure policy debates and their development over time.

  • Use of AI for political analysis (RSS1.05)

    Designed for social scientists without advanced programming abilities, this course explores how to integrate large language models (LLMs) into social science research.

  • Migrant Inclusion: Policies, Practices in the Age of Digitalization (RSS1.19)

    With increasing migration to Western Europe, inclusion and integration have become important topics. This course will provide you with insights into relevant issues on the topic of migrant inclusion in a digitalized society

  • Structural Equation Modelling (RSS1.11)

    In this course, you will learn Structural Equation Modeling (SEM) by doing it. Complicated issues such as estimation and testing are explained intuitively with clear instructions.

  • Panel Data Analysis (RSS1.13)

    This course provides a general survey of the methods used to analyze panel data (i.e., data with units observed repeatedly over time). It places a special emphasis on causal inference, which is a primary goal of social sciences.

  • Randomized Experimental Methods: Survey, Lab, Field and Conjoint Experiments (RSS1.14)

    Experiments have become a standard part of the methodological toolkit of social scientists. In particular, they are well-suited to facilitate casual inferences.

  • Critical discourse Analysis: Texts, contexts and power (RSS1.16)

    This course offers a comprehensive, practical introduction to qualitative CDS.

  • Mixed Methods Research with MAXQDA: Practices and Techniques (RSS2.03)

    This is a practical course designed to assist junior researchers in developing and executing a mixed method research (MMR) project.

  • Big Data: Introduction to Text Analysis (RSS2.07)

    The course will introduce statistical models for network data and other data with complex dependence among observations. A range of models will be discussed theoretically, in application and using R.

  • Introduction into R (RSS2.09)

    Join for a week-long journey into R programming for data analysis. From mastering R basics and data import to advanced skills in cleaning, visualization, and analysis, this course integrates theory with hands-on exercises.

  • Online Qualitative Methods and Virtual Ethnography (RSS00.05)

    This course focuses on the use of digital technologies for qualitative data collection, from a conceptual to applied perspective, including logistical, technical and ethical issues.

  • Crash Course in R – A Gentle Introduction (RSS00.06)

    The course covers the basics of programming in R. Besides that, it also covers working in RMarkdown, which allows you to generate an entire research paper with RStudio.

  • An introduction to the theory and practice of Personal Science (RSS00.08)

    Explore Personal Science this summer! Join our course to discover the fascinating intersection of self-observation and data analysis.

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