Conversation Analysis — Studying Talk-in-Interaction

No. CA requires recordings of naturally occurring interaction — talk that participants would have produced regardless of the researcher's presence. Research interview data are shaped by the interviewer's questions and the interviewee's awareness of being studied; they cannot be analysed as ordinary or institutional conversation. If your data are interviews, discourse analysis or thematic analysis is more appropriate.

There is no fixed minimum. CA typically works with collections of 20–50 or more instances of a specific phenomenon drawn from a corpus of recordings. Some studies build collections from hundreds of instances across many hours of data; others achieve analytic saturation with fewer recordings when the phenomenon is clearly delineated. The key is building a collection large enough to identify systematic patterns and their variant forms, not meeting a participant count.

Yes, if you are conducting CA yourself. Jefferson notation — covering timed pauses, overlap onset and offset, intonation contours, volume, and speech rate — is not optional decoration; it records the very features that CA demonstrates to be interactionally consequential. Many analyses hinge on the exact placement of a 0.3-second pause or the onset of overlap. Commercial transcription services and speech-to-text tools produce orthographic transcripts that are inadequate for CA without substantial specialist revision.

CA and CDA share an interest in talk and text but differ fundamentally in their theoretical commitments. CA suspends assumptions about power, ideology, or social structure, working only from what participants demonstrably orient to in the interaction itself. CDA begins from a critical theoretical framework and uses discourse data as evidence of broader social inequalities or ideological positions. CA practitioners are generally cautious about importing external explanatory frameworks because doing so risks overriding what participants themselves are doing in the data.

CA has been extended to text-based interaction (chat, SMS, social media) and multimodal interaction (video calls, gesture, gaze). These adaptations require modified transcription conventions that capture the medium's specific features — for example, message delivery timing and platform affordances in chat. The core sequential logic of CA applies, but analysts must adapt transcription practices to the modality.