Qualitative Methods in PSaPA (intermediate level) 1600-SZD-SPEC-MJ-PA
The course is structured as a workshop: brief theoretical introductions are immediately translated into work on the material and step-by-step exercises. Participants work individually and in small groups to design research questions, prepare tools (e.g., interview scripts), create categories/codebooks, and perform preliminary coding and interpretation of data, with ongoing feedback and discussion.
During the classes, the following will be discussed and applied in practice: in-depth interviews, and Qualitative Content Analysis (QCA) using M. Schreier’s approach, reflexive thematic analysis. The coursework will include literature discussions, analysis of scientific articles, and practical exercises.
Course coordinators
Type of course
Mode
Prerequisites (description)
Learning outcomes
Knowledge | The graduate knows and understands:
WG_01 - to the extent necessary for existing paradigms to be revised - a worldwide body of work, covering theoretical foundations as well as general and selected specific issues - relevant to a particular discipline
within the social sciences
WG_02 - the main development trends in the disciplines of the social sciences in which the education is provided
WG_03 - scientific research methodology in the field of the social sciences
WK_01 - fundamental dilemmas of modern civilisation from the perspective of the social sciences
Skills | The graduate is able to:
UK_05 - speaking a foreign language at B2 level of the Common European Framework of Reference for Languages using the professional terminology specific to the discipline within the social sciences, to the extent enabling participation in an international scientific and professional environment
Social competences | The graduate is ready to
KO_01 - fulfilling the social obligations of researchers and creators
KO_02 - fulfilling social obligations and taking actions in the public interest, in particular in initiating actions in the public interest
KO_03 - think and acting in an entrepreneurial manner
and others:
Knowledge | The PhD student explains and describes:
selected specific issues related to qualitative methodology (WG_01)
the main development trends in qualitative methodology (WG_02)
qualitative research methodology within the discipline of Political Science and Public Administration (WG_03)
fundamental dilemmas of qualitative methodology (WK_01)
Skills | The PhD student:
uses a foreign language at CEFR level B2, employing specialist terminology appropriate to qualitative methodology, to a degree that enables participation in an international academic and professional environment (UK_05)
Social competences | The PhD student:
fulfils the social responsibilities of researchers in the area of qualitative methodology (KO_01)
Other: PhD students deepen their knowledge of selected qualitative research methods.
Assessment criteria
Description of requirements related to participation in classes, including the permitted number of explained absences: Doctoral students attend the classes and work independently. The classes will take the form of consultations and individual work with the doctoral student. One absence is allowed.
Principles for passing the classes and the subject (including resit session): Active participation in classes, written assessment
Methods for the verification of learning outcomes: Written assessment
Evaluation criteria: Correct execution of tasks
Use of Artificial Intelligence (AI) Tools:
In accordance with Resolution No. 29/2025 of the Didactic Council of the Faculty of Political Science and International Studies, the use of artificial intelligence (AI) tools during this course is regulated based on the AI Assessment Scale (AIAS).
Level of allowed AI use: Level 4 – Critical Evaluation of AI-generated Content
Students are allowed to use AI tools to generate content only for specific tasks. However, all AI-generated outputs must be clearly marked and accompanied by a critical assessment of their accuracy, relevance, and biases. Students must demonstrate their own analytical and reflective input in all assignments.
Permitted uses may include:
Using AI to draft selected parts of a written assignment (with proper citation).
Comparing AI-generated and human-written texts.
Critically evaluating AI-generated content for reliability and bias.
Integrating AI content into broader projects with student-led synthesis.
Important rules:
Any use of AI must be disclosed.
All AI-generated content must be cited in footnotes and acknowledged in the declaration.
Failure to comply with these rules will be treated as a breach of academic integrity.
Students are encouraged to develop their digital and AI literacy by engaging critically with AI tools, while maintaining the standards of academic honesty, autonomy, and ethical responsibility.
Practical placement
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Bibliography
The readings will be provided during the course sessions. The readings for the course may be changed or updated during the semester.
Notes
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Term 2025L:
not applicable |