HD·R1·02 Research
Qualitative Research Methods in Design
Depth over breadth, meaning over measurement.
Qualitative methods generate rich, contextual understanding of why people behave as they do, the research-depth counterpart to the Foundation-level Design Research Methods course. This course covers interviewing, participant observation, and thematic analysis at a rigour level appropriate for scholarly research.
In Development
This course's full module content is being written. The outline below is final, subscribe for updates when it publishes.
LEARNING OUTCOMES
- 01 Design a rigorous qualitative research study appropriate to a scholarly research question
- 02 Conduct semi-structured interviews that generate rich, analysable data
- 03 Apply participant observation methods with appropriate reflexivity
- 04 Conduct thematic analysis systematically rather than impressionistically
- 05 Address rigour criteria specific to qualitative research (credibility, transferability)
- 06 Distinguish scholarly qualitative research rigour from practitioner-level user research
- 07 Write up qualitative findings to scholarly reporting standards
- Rigour criteria distinguish scholarly from practitioner-level qualitative work
- Cross-reference: HD·F2·10 Design Research Methods for the foundation-level practitioner version
- Analysis plan should be considered before data collection, not after
- Sampling strategy should be justified, not merely convenient
- Protocol consistency and flexibility are both required, not a trade-off to pick one side of
- Unexpected threads often carry the most analytically valuable data
- Researcher presence always affects the observed context to some degree
- Reflexive accounting is a rigour requirement, not optional self-reflection
- Documented coding process distinguishes systematic from impressionistic analysis
- Coding decisions should be traceable back to specific data excerpts
- Qualitative rigour criteria are distinct from, not lesser versions of, quantitative criteria
- Evaluating qualitative work by quantitative standards is a category error
- Traceability from data to conclusion is a core scholarly writing requirement
- Opaque analytical leaps undermine reader trust in qualitative findings