Complementary Methods Program in the Social Sciences (PhD)
Successful social science practice requires sound methodological skills that enable students to keep up with formal, thematic, and methodological developments within the social science disciplines. To ensure that doctoral students in the social sciences remain academically competitive in the future, the Subject Board Social Sciences has decided to revise its complementary methods program (CMP).
Goals and Objectives
- To strengthen and consolidate the academic performance of doctoral students in the social sciences.
- To strengthen the interdisciplinary exchange among doctoral students in the social sciences.
- To integrate methodological excellence into the research and teaching profiles of the participating departments (IKMZ, IPZ, ISEK, SUZ).
Course Structure
The CMP continues to be based on two types of courses:
- Ad hoc courses are based on the current needs of the doctoral projects and the interests of the doctoral students. The doctoral student representatives of the subject group regularly survey their needs and desires.
- Cyclical courses address software- and data-related changes that are increasingly shaping the social sciences. Knowledge transfer skills (Module E) are now also considered. Specifically, this includes the following recurring modules:
Modul A: From Program to Paper: How to Organize and Document Data and Analysis is dedicated to modern forms of work organization, data documentation, and publication. It forms the important gateway between empirical analysis and publication and serves the growing demand for replication in the social sciences.
Modul B covers techniques of modern quantitative data analysis:
- R for Basic Social Science Statistics provides a practical introduction to basic statistical analysis techniques for survey and macro data. The didactic focus is on teaching application skills in R.
- Advanced Social Sciences with R is designed to establish an advanced quantitative method culture based on the extended application potential of R in the area of complex digital data sets. In addition to computational social science techniques, approaches from network analysis or Bayesian statistics can be presented.
Modul C: Data Visualization deals with new models for visualizing data and research results. It addresses the paradigm shift from static, tabular presentations to graphical visualization of statistical and qualitative results (including moving images).
Modul D is dedicated to the specific needs of qualitative research:
- Qualitative Data Collection and Analysis deals with qualitative methods and designs in social science research. It supports doctoral students in collecting non-standardized observation, text, image or video data and in extracting patterns and/or meanings from them. In addition to a fo-cus on individual qualitative methods, in-depth options are also offered.
- Ethnographic Writing helps doctoral students write about non-standardized observational, textual, image, or video data and the patterns and meanings derived from them in their research. Emphasis is placed on the relationship between method and representation, as well as on the political and ethical implications of textual representation.
Modul E: Resonance Skills helps doctoral students communicate their research for specific audiences. Writing strategies for publications in social science journals and/or specialist publishers, which are essential for academic qualification, are deepened. In addition, doctoral students are tested in dealing with different (news) media and how they can confidently contribute their specialist expertise to the public debate.
Organization
- Courses are usually offered as workshops in block format. This form of organization creates space for the problem-oriented application of the material and thus creates added value for the individual dissertation project and/or the methodological training of the doctoral students.
- In principle, 1 ECTS is awarded for each course. Depending on the amount of preparation and follow-up work, more ECTS may be awarded. For reference: 1 ECTS corresponds to 25-30 hours of work.
- The language of the courses is usually English.
- Courses are taught by internal and external lecturers who are qualified in both methodology and social sciences.
- All courses are evaluated by the course participants. The PhD coordinator Social Sciences evaluates the surveys and makes them available to the course instructors for discussion with the participants and, if necessary, to the module coordinators. At the same time, they are digitally archived at the Graduate School Office.
- All courses are published in the UZH course catalog and announced by the PhD coordinator Social Sciences.
- The Subject Board Social Sciences reserves the right to change the content and/or structure of the CMP at any time.
Courses
Spring Semester 2025
Gesellschaftstheoretische Perspektiven auf gesellschaftliche Transformationsprozesse (24 and 25 February)
Beliefs, Behaviors, and Society: Understanding Social Dynamics in a Digital Age (13-14 March)
Mixed Methods: Combining Qualitative and Quantitative Research Methods Productively (8-9 May)
Digital Ethnography Workshop: Focus on Ethics (27-28 May)
Advanced Survey Research (2-6 June)
Workshop Scientific English Writing (5 and 12 June half day, 19 June, online)
Writing for Publication in Top Journals (17-18 June
Fall Semester 2024
Workshop Scientific English Writing
Multilevel Modelling and Analysis
Die Macht der Schlagzeile: Effektive Medienkommunikation in den Sozialwissenschaften
Opportunities and Risks of AI in the Social Sciences
Communicating Science Online: Social Media
Data Visualization: Principles, Practices and Applications
From Design to Paper: Make Your Research Fully Reproducible
Advanced Social Sciences with R
Doctoral Thesis Writing Workshop for Qualitative Social Scientists
Spring Semester 2024
Writing an ethnographic article: structure - argumentation - flow
Mixed Methods: Choosing and combining research methods productively
Multi-modal qualitative research methods
R for Basic Social Science Statistics
Reproducible Research in Social Science: R Studio and Git
Language Models in Practice
Souveränes Argumentieren in kontroversen Situationen
Network Analysis in Psychology and Social Sciences
Fall Semester 2023
- Workshop Scientific English Writing
- Advanced Social Sciences with R
- Souveränes Argumentieren in kontroversen Situationen
- Writing for Publication in Top Journals
- From Design to Paper: Make Your Research Fully Reproducible
- Data Visualization: Principles, Practices and Applications
- Introduction to data analysis with MAXQDA
Spring Semester 2023
- Open Science in Social Sciences: Controversy, Crisis and Change (6, 13 and 20 February, half-day)
- Basic Social Science Statistics (2, 6, 13 and 16 March, half-day, online)
- Creative Techniques for Thesis Writing (3 and 17 March, full day)
- Mixed Methods: Choosing and combining research methods productively (9 and 10 March, full day)
- Empirical Video Analysis for Social Scientists: Basics (29 and 31 March, half-day)
- Empirical Video Analysis for Social Scientists: Computational Approaches (14 and 16 June, half-day)
- Wissenschaftskommunikation der Sozialwissenschaften (13, 20 and 27 June, half-day)
Fall Semester 2022
- From design to paper
- R for Advanced Social Sciences
- Data Visualization and Statistical Graphs with R
- Die Methode des ethnografischen Schreibens als Erkenntnisprozess
- Workshop Scientific English Writing
- Die Macht der Schlagzeile: Effektive Medienkommunikation in den Sozialwissenschaften
- Democracy Studies Advanced Module: Crisis of Democracy
- Swiss Summer School in Democracy Studies
- Democracy Goes to School
Spring Semester 2022
- Writing for Publication in Top Journals
- Basic Social Science Statistics with R
- Democracy Studies Module II: Empirical Approaches to Democracy
- Überzeugender Medienauftritt: Botschaften verständlich und klar formulieren
- Understanding Polls
- Qualitative Inhaltsanalyse mit MAXQDA
- Mixed Methods: qualitative und quantitative Daten sinnvoll kombinieren
Fall Semester 2021
- Advanced Statistics with R
- Data Visualization and Statistical Graphics with R
- Web Science Research in Cultural Studies and the Social Sciences
- Multilevel Modelling and Analysis
- Crashkurs "Verständliches und attraktives Texten für ein Massenpublikum"
Spring Semester 2021