| Course information | |
|---|---|
| Period | Block 1 |
| Timeline | End-November - mid-December |
| Number of ECTS | 2,5 ECTS |
| Coordinator | Dr Bahar Sakizlioglu |
| Methodology | Workshops, lectures, self-study |
Course description
This course introduces students to the design, practice, and analysis of qualitative research in the field of urban studies. It provides a compact but comprehensive foundation, covering both qualitative data collection and qualitative data analysis.
The course is structured in two integrated parts:
Qualitative Data Collection Methods:
Students will learn two main data collection methods in urban research: in-depth interviews, participatory urban observation. Each method will be discussed in terms of research design, sampling strategies, ethics, and the opportunities and limitations it offers in urban fieldwork. Through workshops, students will practice their skills and make exercises, developing practical experience in collecting qualitative data.
Qualitative Data Analysis for Urban Research
The second part introduces students to qualitative data analysis, more specifically to thematic analysis and coding as key approaches to making sense of qualitative data. Using CAQDAS (Computer Assisted Qualitative Data Analysis Software), students will learn how to import, code, and interpret qualitative data in a structured and systematic way. Building on these foundations, the course also introduces students to the emerging role of GenAI in qualitative analysis. The students will critically explore how GenAI can assist with qualitative analysis. The emphasis will be on understanding both the opportunities and the limitations of GenAI in qualitative research, particularly in relation to validity, transparency, bias, and the interpretative role of the researcher.
Learning objectives
By the end of the course, students will have a working knowledge of the qualitative research methods and analysis for urban contexts. They will also develop an informed perspective on the use of GenAI-assisted analysis, enabling them to combine practical skills with critical reflection when designing and conducting their own thesis research. By the end of the course, students will be able to:
- To understand theoretical and practical aspects of conducting qualitative urban research.
- Design and conduct interviews.
- To analyze qualitative data using coding and thematic analysis in CAQDAS
- To critically reflect on qualitative research findings.
- Apply research ethics and rigor in qualitative research.
- Critically assess the role of AI in qualitative analysis, including its applications, opportunities, limitations and ethical challenges.
Course content
The course will cover the following sessions:
- Session 1: Introduction to Qualitative Research in the Urban
- Session 2: Interviews in urban research
- Workshop: Interviewing Skills 4) Session 3: Urban Observations
- Session 4: Making sense of qualitative data
- Session 5: Qualitative Coding and Data Analysis I
- Workshop: Qualitative Coding and Data Analysis I
- Session 6: Qualitative Coding and Data Analysis II
- Workshop: Qualitative Coding and Data Analysis II
- Workshop: Qualitative Coding and Data Analysis III
Learning methods
The course has a hands-on approach, where students understand and practise the elements of qualitative research, data collection, data analysis and effectively communicating qualitative research findings.
Lectures on data collection and analysis will be accompanied with workshops to practice the gained skills throughout the course. In addition to face to face sessions and workshops, additional video tutorials are made available for self-study. During the assigned self-study hours, the participants are expected to prepare for the lectures and work on their assignments. The course offers a Q&A session to give students chance to ask questions to improve their learning process. The course also offers exercises, quizzes, study material on Canvas not only to assess the prior knowledge of the participants but also prepare them for classroom discussions.
The participants will have to read articles and book chapters and see educational videos before the lectures. Course materials are to be found in RefWorks and on Canvas.