🤖 AI Summary
This study addresses the limitations of conventional interview analysis, which relies heavily on researchers’ specialized qualitative skills, is difficult to scale, and often overlooks participants’ own interpretive logics. To overcome these challenges, the paper proposes the Documentary Mode of Interpretation (DMI), a membership-based method grounded in ordinary people’s natural language competencies. DMI identifies endogenous themes within interview texts to uncover the collective reasoning processes participants employ in making sense of the research topic. Crucially, this approach requires neither predefined coding frameworks nor formal training in social theory, thereby departing from traditional qualitative paradigms by generating themes bottom-up from participants’ perspectives. By significantly lowering the technical and epistemic barriers to qualitative analysis, DMI offers non-specialist researchers an accessible, low-threshold pathway to effectively interpret the collective meaning structures embedded in interview data.
📝 Abstract
Interviews are commonplace in HCI. This paper presents a novel documentary method of interpretation that supports analysis of the topics contained within a collection of transcripts, topics that are endogenous to it and which elaborate participants collective reasoning about issues of relevance to research. We contrast endogenous topic analysis with established qualitative approaches, including content analysis, grounded theory, interpretative phenomenological analysis, and thematic analysis, to draw out the distinctive character of the documentary method of interpretation. Unlike established methods, the DMI does not require that the analyst be proficient in qualitative analysis, or have sound knowledge of underlying theories and methods. The DMI is a members method, not a social science method, that relies on mastery of natural language; a competence most people possess.