Compulsory PhD courses

We expect that most students will take these courses during their first years, if matched in time. A fourth course is also compulsory: “Introduction to teaching” (3 ECTS), which is supported by Stockholm University. If a student intends to do teaching during the programme, the course "Professional development course 1 - Teaching and Learning" (UL1), 7.5 ECTS, is suggested instead.

Course 1. Sustainability Science and Biosphere Stewardship (5 credits, October 2022)

Course leaders: Magnus Nyström & Robert Blasiak

This course is an introduction to advanced research studies for new doctoral students enrolled in SRC’s PhD programme. It communicates the research framework that is used by centre researchers, clarifies “ways of thinking and practicing” and “tacit knowledge” at SRC, i.e. the “softer” values that are essential for multi- and transdisciplinary advanced research at SRC.

The course identifies outstanding major research challenges and research fronts in order for the PhD student to be able to understand where they can situate their research, and also how their research can contribute to developing any or several of these fronts. The course also relate the SRC “branch” of Sustainability Science to a wider historical context including fields of philosophy of science and provide practical advice on methods and approaches that will guide the PhD student in his or her research. The final exercise and examination is aimed to help the PhD student take a step forward in its current research paper/project.

The course aims to take on a number of questions that many students ask themselves, including: What is it that is special with SRC, How are scientists working here, How can I fit in, What is my contribution, and How should I focus my work right now?

Course 2. Qualitative Methods for Researchers in Sustainability Science (7.5 credits, next course 2024)

Course leader: Beatrice Crona

The purpose of this course is to introduce doctoral students to field methods and qualitative data analysis, including such methods as unstructured interviews and observation. Students will become acquainted with the epistemology of qualitative approaches and with developing skills in all areas of qualitative methodology, through first-hand experience of using these methods to collect and analyze data on an appropriate topic. Students will be expected to identify such a topic and research question early in the course (with the help of the instructor), and carrying out a qualitative research project over the duration of the course. The course will be based around a set of classroom sessions with support from SRC researchers, each followed by interim clinics where students will get feedback on their assignments and have the opportunity to discuss this with senior researchers at SRC.

Classroom work will be focused on developing the skills required for initiating and completing a successful qualitative evaluation. Readings are assigned to help students understand the background and context of qualitative research and the relationship of these research methods to skillful and phase appropriate evaluation. In some cases we will visit nearby locales to allow students to collect observations and then to receive feedback on the quality and appropriateness of their observations. This kind of experiential approach has been shown to produce a visible level of skill acquisition. Such skill acquisition can simply not be acquired through extensive readings, and although such reading will be supplied in order to provide students with the resource of a variety of back-up tools, these readings will not provide the focus of the seminar.

Students will be expected to undertake a "pilot" qualitative research project for the purpose of the course ─ whether individually or as a team with others ─ in an arena of interest and to submit the assigned exercises, such as field notes, on specified dates throughout the course. These notes, student field experiences and the assigned readings are the materials around which seminar discussions will be organized.

Those doctoral students who intend to use qualitative data as a basis for dissertation work, will find this course of value in both broadening and deepening their skill base. Those who do not currently have plans to use qualitative interviews will find value by deepening their understanding of, and ability to assess, qualitative methods used by other colleagues in transdisciplinary environmental research and broadening their understanding of, the types of questions that can be asked and answered with such methods

Course 3. Quantitative Methods for studying Social-Ecological Systems (4 credits, next course 2023)

Course leader: Maja Schlüter

Ten days over two weeks, one lecture a day (morning), one workshop a day (afternoon), with a guest lecture on the first Wednesday and reading throughout. Lectures will focus on key social-ecological concepts and the language and approaches quantitative scientists use to describe and analyze them. Key point – in the topics listed below, we are not going to teach how things are done. Rather we are looking to make students aware that these approaches exist, and give them the language with which to talk to experts in these approaches.

Core Concepts
1) Philosophy of quantitative analysis and research design
2) Numbers, data, exploratory data analysis and statistics
3) Dynamical systems (equilibrium, stability, stocks and flows)
4) Complex Adaptive Systems (networks, emergence, self-organization, agent- based modeling)
5) Human Decision Making: optimization, game theory, adaptation and learning

Learning outcomes
• Understanding of how to conduct quantitative analysis of Social-Ecological Systems (SESs), and how to model (in the broadest sense)

• A vocabulary to talk with researchers doing ecological, economic, social- ecological modeling of SES using statistical, mathematical or computational approaches

• Overview of quantitative methods available for studying SES, particularly formal modeling, empirical analysis and methods from complexity science

• Understanding of when and how different approaches can be used, their potentials and limitations (but no technical details on their application)

• Understanding of different conceptualizations of SES, different approaches and their implications (e.g. what do we learn from a theoretical model, from a statistical analysis, etc.)