27/04/2026
[Seminar talk] What can interview data tell us that a Likert scale cannot?
I spoke recently at the CPCECPR Seminar on making sense of qualitative interview data. The core question I kept returning to: if we want to understand how and why students or teachers experience what they do, do our methods actually match that ambition?
Drawing on three of my own studies — on EAP teacher identity, digital storytelling service-learning, and generative AI in writing — I shared:
1. Why interpretivist researchers treat data as meaning-making, not measurement
2. How interview protocol design must stay answerable to your research questions
3. What "trustworthiness" means in practice: member checking, triangulation, positionality
4. Why coding interview data is both bottom-up (open coding) and theory-driven (a priori coding)
I also left a question I can't fully answer yet: when AI can code transcripts and write up findings, what is the researcher's distinctive contribution?
My tentative answer, though cliché: it's the enjoyment of the chats. The enjoyment of discovering new insights from the chats -- and WE FIGURE THEM OUT.
Thanks to the LTQC and CPCECPR teams for the invitation.
What is your take — what does the human researcher do that AI cannot, in qualitative work?
(From the talk I gave at CPR Ignite: Student/Staff Research Seminar Series on 27 April 2026. The content is mine, polished into this post with Perplexity AI.) When students come to us for advice on…