Successful practice in the social sciences requires profound knowledge of research methods, which in turn enables the connection to formal, thematic and methodological developments in the social science disciplines. In order to ensure the academic performance and competitiveness of social science doctoral students, the Subject Board Social Sciences of the Graduate School has decided to complement its chair- and project-based doctoral training with a cross-institutional methods program (IKMZ, IPZ, ISEK, SUZ).
- Improving the scientific performance of doctoral students in the social sciences
- Increasing the interdisciplinary exchange among doctoral students in the social sciences
- Systematic integration of methodological excellence in the research and teaching profiles of the social science institutes
The cross-institutional methods program consists of both recurring and singular events. The flex-ible part is tailored to the current needs and demands of the doctoral students in the social sciences. It will be assessed on a regular basis through surveys conducted by the doctoral coordinator in the social sciences and/or by the doctoral student representatives in the Subject Board Social Sciences. The following modules form the stable pillar of the methods program:
Module A: "From program to paper: how to organize and document data and analysis”
This module is devoted to contemporary forms of work organization, data documentation and publication. It defines the link between empirical analysis and publication and serves the growing replication demands in the social science disciplines.
Module B1: "Basic social science statistics with R”
This module provides an introduction to the practical implementation of basic statistical analyses using simple survey or macro data. It is specifically aimed at doctoral students without substantive prior methodological knowledge.
Module B2: "R for advanced social sciences”
This module is aimed at statistically qualified doctoral students without experience in R, but competences in other relevant statistical programs (SPSS, Stata or SAS). It establishes an advanced quantitative method¬ology culture and strengthens the statistical cooperation skills of doctoral students within and between the social science institutes at UZH.
Module C: "Data visualization”
This module deals with new models for the visualization of data and research results. The paradigmatic shift from static, tabular presentations to the graphical representation of statistics will be addressed here.
Module D: "Qualitative data collection, analysis and writing”
This module deals with qualitative methods and designs in social science research. It supports doctoral students in collecting non-standard text and/or image data, extracting patterns and/or meanings from them and articulating them in research papers.
All events are taught by experts from the UZH and other universities. These experts are both methodologically qualified and trained in the social sciences. They are able to link the motivated technical skills to specific subjects and problems of the social sciences.
All courses and workshops are blocked events, whereby the timing and patterns are based on didactic and organizational considerations. There will always be enough room for problem-oriented application of the material.
The working language is usually English, so that - in line with the internationalization strategy of the UZH - non-German-speaking doctoral students can also participate in the events.
All courses and workshops are announced in the UZH course catalog and via the doctoral coordinator in the social sciences. For reasons of quality assurance, they are evaluated anonymously and in a standardized manner by the course participants.
- 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
- 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“
- Workshop: English Academic Writing for Social Scientists