Qualitative Data Analysis Methods

This advanced course is for students in any program who will analyze qualitative data either to prepare more structured data collection methods (e.g. questionnaires) or to draw conclusions directly. Historically ⅔ of the participants are MSc and the remainder are PhDs. Participants come from all chair groups. The course starts with a quick and fundamental review of the nature of subject response data and the role of the researcher in lending meaning. The techniques covered are argument, content, conversation, metaphor, domain, and membership categorization analysis. These are then combined to support systematic review, frame analysis, situational analysis, discourse analysis. The use and limits of models (e.g. LLMs) for these methods are discussed and their exploration is supported.

• Understand and internalise the characteristics of qualitative (subject response) data

• Understand the data requirements of each analysis method taught

• Internalise the nature and limitations of the conclusions each method supports

• Assess and internalise the role of the researcher in lending meaning

• Assess the validity and the reliability of the conclusions produced by each method

• Independently use and improve mastery of software introduced

• Estimate the level of effort required for execution of an analysis and adapt appropriately

• Independently create a synthesis of approaches and apply it using appropriate software

• Assess the usefulness of what has been learned in light of participants' own project

• Apply what has been learned to their own project

• Create and apply an appropriate protocol for transparent reporting

• Assignment report (40%) Analysis of agreed data using agreed methods taught in the course. Resubmissions handled following normal university mechanisms.
• Performance (20%) Lab work. Resubmissions handled following normal university mechanisms.
• Written test with open questions (40%) Re-exam handled following normal university mechanisms.

Research design (YSS20306 / YRM20306 or disciplinary equivalent)

• Links to resources will be provided by the lecturer.