On the 28th November 2019 I’m running this workshop in London for the National Centre for Research Methods with Pat Healey, Matthew Tobias Harris,Claude Heath, and Sophie Skach, which focuses on drawing as a method in interaction analysis. It’s open to any researcher and/or draftsperson – regardless of experience with conversation analysis or drawing. The aim is to introduce artists and social scientists to each other’s methods for visual analysis, inductive observation and inscription of research objects. Places are limited, so please sign up at the link below:
Analysing embodied interaction enables researchers to study the qualitative details of communication and to do reliable coding of interaction for quantification. Some researchers use video stills and word processing software to add arrows and highlights. Others use simple sketches or tracings to present their research findings in their final published results. However, until now, no dedicated courses have been offered that teach drawing as a method for the transcription and analysis of social interaction.
This one-day course will introduce researchers to the theory and method of conversation analysis, and to new graphical tools, transcription methods, and software systems that are available for multimodal analysis of audio-visual data. It will involve short presentations, group discussions and practical work including video data gathering, transcription and analysis. No special equipment is required, although we encourage participants to bring some means of recording video (e.g. a phone or other digital camera).
This course is aimed at researchers across disciplines with an interest in face-to-face social interaction and communication (human or animal, face-to-face or video-mediated). No prior experience of drawing or conversation and discourse analysis is necessary, since we will cover the basics required to learn independently.
Learning outcomes
This course will introduce you to methods, techniques and tools for analysing embodied social interaction.
The course covers:
Conversation analytic methods for collecting, transcribing and analysing video data.
Drawing techniques for use in field notes and in exploratory data analysis sessions.
How to create and use multimodal transcripts for data analysis and presentation of results.
Software tools for creating and sharing computer-readable graphical transcriptions.
Future directions for multimodal interaction analytics e.g. automation and open science.
The Drawing Interactions project aims to develop new graphical techniques and tools for the transcription, analysis and presentation of research into social interaction.
The Drawing Interactions Prototype App (& source code)
In conversation analytic research, Jeffersoniantranscripts of talk are usually used with traced outlines or video stills, and these techniques primarily focus on presenting polished research findings for finished publications. But what about the exploratory phases of research such as initial transcription or collaborative inspection at data sessions? The drawing interactions project uses traditional artistic still life and figure drawing techniques and detailed studies of analysts’ work practices as key starting points to inform the development of graphical tools and techniques for the transcription, analysis and presentation of social interaction.
Sophie Skach leading the workshop at New Directions in Ethnomethodology
Here’s the workshop abstract:
Ethnomethodological and conversation analytic (EM/CA) studies often use video software for transcription, analysis and presentation, but no such tools are designed specifically for EM/CA. There are, however, many software tools commonly used to support EM/CA research processes (Hepburn & Bolden, 2016 pp. 152-169; Heath, Hindmarsh & Luff 2010 pp. 109-132), all of which adopt one of two major paradigms. On the one hand, horizontal scrolling timeline partition-editors such as ELAN (2017) facilitate the annotation of multiple ‘tiers’ of simultaneous activities. On the other hand, vertical ‘lists of turns’ editors such as CLAN (Macwhinney, 1992) facilitate a digital, media-synced version of Jefferson’s representations of turn-by-turn talk. However, these tools and paradigms were primarily designed to support forms of coding and computational analysis in interaction research that have been anathema to EM/CA approaches (Schegloff 1993). Their assumptions about how video recordings are processed, analyzed and rendered as data may have significant but unexamined consequences for EM/CA research. This 2.5 hour workshop will reflect on the praxeology of video analysis by running a series of activities that involve sharing and discussing diverse EM/CA methods of working with video. Attendees are invited to bring a video they have worked up from ‘raw data’ to publication, which we will re-analyze live using methods drawn from traditions of life drawing and still life. A small development team will build a series of paper and software prototypes over the course of the workshop week, aiming to put participants’ ideas and suggestions into practice. Overall, the workshop aims to inform the ongoing development of software tools designed reflexively to explore, support, and question the ways we use video and software tools in EM/CA research.
References
ELAN (Version 5.0.0-beta) [Computer software]. (2017, April 18). Nijmegen: Max Planck Institute for Psycholinguistics. Retrieved from https://tla.mpi.nl/tools/tla-tools/elan/
Heath, C., Hindmarsh, J., & Luff, P. (2010). Video in qualitative research: analysing social interaction in everyday life. London: Sage Publications.
Hepburn, A., & Bolden, G. B. (2017). Transcribing for social research. London: Sage.
MacWhinney, B. (1992). The CHILDES project: Tools for analyzing talk. Child Language Teaching and Therapy, (2000).
Schegloff, E. A. (1993). Reflections on Quantification in the Study of Conversation. Research on Language & Social Interaction, 26(1), 99–128.
See the offprint: Albert, S., & Raymond, C. W. (2019). Conversation analysis at the ‘middle region’ of public life: Greetings and the interactional construction of Donald Trump’s political persona. Language & Communication, 69, 67–83. https://doi.org/10.1016/j.langcom.2019.08.001
We’re contributing this talk to Josh Raclaw‘s panel at the AAA 2017 Toward a transdisciplinary coalition in sociocultural linguistics: A collaborative analysis of presidential discourse in Trump’s Black History Month Listening Session. The panel invites scholars from a variety of methodological orientations to address the same bit of data. Our EM/CA-oriented contribution to the panel focuses on the greeting sequences in the first few moments of the meeting.
This paper is designed as a contribution to an inter- and trans-disciplinary panel investigating President Donald Trump’s Black History Month Listening Session. Here we adopt the theory and method of conversation analysis (CA) to examine the first minute of this multiparty interaction—from Trump’s entrance into the room, to the launch of his prepared remarks. Greetings and other phenomena that occur during interactional openings have been widely studied from a conversation-analytic perspective (see, e.g., Schegloff, 1968), and yet here we see them occurring in a very particular institutionalized setting, with very particular participants, and in the presence of an overhearing audience (i.e., at-home viewers). In this paper, our aim is to unpack Trump’s initial interactions with those present in the room: whom does he greet, and in what ways, and how is he greeted in return? Moreover, we ask how these greeting practices contribute to the business of “‘doing being’ president” (cf. Sacks, 1984), and thus we will discuss the various membership categories (Sacks, 1992) that are made relevant in and through these brief introductory exchanges. Our analysis therefore offers insights not only into this specific individual’s interactional style and this particular setting, but also into how greetings operate more broadly in multiparty discourse of this sort.
References
Albert, E. (1964). “Rhetoric,” “logic,” and “poetics” in Burundi: culture patterning of speech behavior. American Anthropologist, 66, pt 2(6), 35-54.
Billig, Michael. (1999a). Conversation Analysis and the claims of naivety. Discourse & Society, 10(4), 572-576.
Billig, Michael. (1999b). Whose terms? Whose ordinariness? Rhetoric and ideology in Conversation Analysis. Discourse & Society, 10(4), 543-582.
Clayman, Steven E., & Heritage, John. (2002). The News Interview: Journalists and Public Figures on the Air. Cambridge, England: Cambridge University Press.
Clift, Rebecca, & Raymond, Chase Wesley. (2018). Actions in practice: On details in collections. Discourse Studies.
Couper-Kuhlen, Elizabeth. (1984). A new look at contrastive intonation. In R. J. Watts & Urs Weidmann (Eds.), Modes of Interpretation: Essays presented to Ernst Leisi on the occasion of his 65th birthday (pp. 137-158). Tübingen: Gunter Narr.
Couper-Kuhlen, Elizabeth, & Thompson, Sandra A. (2005). A linguistic practice for retracting overstatements: ‘Concessive repair’. In Auli Hakulinen & Margret Selting (Eds.), Syntax and Lexis in Conversation: Studies on the use of linguistic resources in talk-in-interaction (pp. 257-288). Amsterdam: John Benjamins.
Drew, Paul, & Heritage, John. (1992). Analyzing Talk at Work: An Introduction. In Paul Drew & John Heritage (Eds.), Talk at Work (pp. 3-65). Cambridge: Cambridge University Press.
Heritage, John. (1984). Garfinkel and Ethnomethodology. Cambridge, UK: Polity Press.
Heritage, John, & Clayman, Steven E. (2010). Talk in Action: Interactions, Identities and Institutions. Oxford: Blackwell-Wiley.
Hough, Emerson. (1917). The man next door. New York: D. Appleton and Company.
Hansen, A. D. (2005). A practical task: Ethnicity as a resource in social interaction. Research on Language and Social Interaction, 38(1), 63-104.
Jefferson, Gail. (1978). What’s In a ‘Nyem’? Sociology, 12, 1, 135-139.
Jefferson, Gail. (1981). The Abominable ‘Ne?’: A Working Paper Exploring the Phenomenon of Post-Response Pursuit of Response. Occasional Paper No.6, Department of Sociology,: University of Manchester, Manchester, England.
Jefferson, G. (1989). Letter to the editor re: Anita Pomerantz’ epilogue to the special issue on sequential organization of conversational activities. Western Journal of Speech Communication, 53, 427-429.
Kendon, Adam. (1990). Spatial Organization in Social Encounters: The F-Formation System. In Adam Kendon (Ed.), Conducting Interaction: Patterns of Behavior in Focused Encounters (pp. 209-238). Cambridge: Cambridge University Press.
Pomerantz, Anita M. (1984a). Agreeing and Disagreeing with Assessments: Some Features of Preferred/Dispreferred Turn Shapes. In J. Maxwell Atkinson & John Heritage (Eds.), Structures of Social Action: Studies in Conversation Analysis (pp. 57-101). Cambridge, UK: Cambridge University Press.
Pomerantz, Anita M. (1984b). Pursuing a Response. In J. Maxwell Atkinson & John Heritage (Eds.), Structures of Social Action (pp. 152-164). Cambridge: Cambridge University Press.
Raymond, Chase Wesley. (2017). Indexing a contrast: The ‘do’-construction in English conversation. Journal of Pragmatics, 118, 22-37.
Raymond, Chase Wesley. (Frth). Category accounts: Normativity in sequences of action. Language in Society.
Raymond, Chase Wesley, & Stivers, Tanya. (2016). The omnirelevance of accountability: Off-record account solicitations. In Jeffrey D. Robinson (Ed.), Accountability in Social Interaction (pp. 321-353). Oxford: Oxford University Press.
Raymond, Geoffrey. (2018). Which epistemics? Whose conversation analysis? Discourse Studies.
Rossano, Federico. (2009). Gase as a method of pursuing responses. Paper presented at the Annual Meets of the American Sociological Association, San Franciso.
Rossano, Federico. (2013). Gaze in Social Interaction. In Jack Sidnell & Tanya Stivers (Eds.), The Handbook of Conversation Analysis (pp. 308-329). Malden, MA: Wiley-Blackwell.
Sacks, Harvey. (1984). Notes on Methodology. In J. M. Atkinson & J. Heritage (Eds.), Structures of Social Action (pp. 21-27). Cambridge: Cambridge University Press. (Edited by Gail Jefferson from various lectures).
Sacks, Harvey. (1987 [1973]). On the Preferences for Agreement and Contiguity in Sequences in Conversation. In Graham Button & John R. E. Lee (Eds.), Talk and Social Organisation (pp. 54-69). Clevedon, England: Multilingual Matters.
Sacks, Harvey. (1992). Lectures on Conversation (2 vols.). Oxford: Blackwell.
Sacks, Harvey, Schegloff, Emanuel A., & Jefferson, Gail. (1974). A Simplest Systematics for the Organization of Turn-Taking for Conversation. Language, 50, 696-735.
Schegloff, Emanuel A. (1968). Sequencing in Conversational Openings. American Anthropologist, 70, 1075-1095.
Schegloff, Emanuel A. (1987a). Analyzing Single Episodes of Interaction: An Exercise in Conversation Analysis. Social Psychology Quarterly, 50(2), 101-114.
Schegloff, Emanuel A. (1987b). Between Macro and Micro: Contexts and Other Connections. In Jeffrey C. Alexander, Bernhard Giesen, Richard Münch, & Neil J. Smelser (Eds.), The Micro-Macro Link (pp. 207-234). Berkeley: University of California Press.
Schegloff, Emanuel A. (1997a). Practices and Actions: Boundary Cases of Other-Initiated Repair. Discourse Processes, 23(3), 499-545.
Schegloff, Emanuel A. (1999b). ‘Schegloff’s Texts’ as ‘Billig’s Data’: A Critical Reply to Billig. Discourse and Society, 10(4), 558-572.
Schegloff, Emanuel A. (2007). Sequence organization in Interaction: A Primer in Conversation Analysis Volume 1. Cambridge: Cambridge University Press.
Schegloff, Emanuel A., & Sacks, Harvey. (1973). Opening Up Closings. Semiotica, 8(4), 289-327.
Stivers, Tanya. (2005). Modified Repeats: One Method for Asserting Primary Rights from Second Position. Research on Language and Social Interaction, 38(2), 131-158.
Stivers, Tanya, & Rossano, Federico. (2010). Mobilizing Response. Research on Language and Social Interaction, 43, 3-31.
Here these are the references for my talk on terminal assessments and performance evaluations in a partner dance workshop at LANSI 2017.
I’ve given up trying to fit them all onto one slide at the end – so here they are on this web page. A recording of the talk is to follow – here’s one of the slides for now:
A slide showing some of the normative dimensions of accountability that emerge through students’ terminal performance evaluations and how they’re involved in teachers’ routine, terminal assessments.
References
Albert, S. (2015). Rhythmical coordination of performers and audience in partner dance. Delineating improvised and choreographed interaction. Etnografia E Ricerca Qualitativa, 3, 399–428. https://doi.org/10.3240/81723
Antaki, C. (2002). “Lovely”: Turn-initial high-grade assessments in telephone closings. Discourse Studies, 4(1), 5–23. https://doi.org/10.1177/14614456020040010101
Antaki, C. (2000). “Brilliant. Next Question…”: High-Grade Assessment Sequences in the Completion of Interactional Units. Research on Language & Social Interaction, 33(3), 37–41. https://doi.org/10.1207/S15327973RLSI3303_1
Broth, M., & Mondada, L. (2013). Walking away: The embodied achievement of activity closings in mobile interaction. Journal of Pragmatics, 47(1), 41–58. https://doi.org/10.1016/j.pragma.2012.11.016
Broth, M., & Keevallik, L. (2014). Getting Ready to Move as a Couple: Accomplishing Mobile Formations in a Dance Class. Space and Culture, 17(2), 107–121. https://doi.org/10.1177/1206331213508483
De Stefani, E., & Mondada, L. (2013). Reorganizing Mobile Formations: When “Guided” Participants Initiate Reorientations in Guided Tours. Space and Culture, 17(2), 157–175. https://doi.org/10.1177/1206331213508504
De Stefani, E., & Gazin, A.-D. (2014). Instructional sequences in driving lessons: Mobile participants and the temporal and sequential organization of actions. Journal of Pragmatics, 65, 63–79. https://doi.org/10.1016/j.pragma.2013.08.020
Garfinkel, H. (1967). Studies in ethnomethodology. America. Englewood Cliffs, New Jersey: Prentice-Halll.
Goodwin, C., & Goodwin, M. H. (1992). Assessments and the construction of context. In C. Goodwin & A. Duranti, C. Goodwin & A. Duranti (Eds.), Rethinking context: Language as an Interactive Phenomenon (Vol. 11, pp. 147–189). Cambridge: Cambridge University Pressess.
Harness Goodwin, M., & Goodwin, C. (1986). Gesture and coparticipation in the activity of searching for a word. Semiotica, 62(1–2), 51–75.
Keevallik, L. (2010). Bodily Quoting in Dance Correction. Research on Language & Social Interaction, 43(4), 401–426. https://doi.org/10.1080/08351813.2010.518065
Keevallik, L. (2013). Here in time and space: Decomposing movement in dance instruction. In P. Haddington, L. Mondada, & M. Nevile, P. Haddington, L. Mondada, & M. Nevile (Eds.), Interaction and Mobility: Language and the Body in Motion (pp. 345–370). Berlin/Boston: Walter de Gruyter.
Oshima, S., & Streeck, J. (2015). Coordinating talk and practical action: The case of hair salon service assessments. Pragmatics and Society, 6(4), 538–564. https://doi.org/10.1075/ps.6.4.04osh
Pomerantz, A. (1984). Agreeing and disagreeing with assessments: Some features of preferred/dispreferred turn shapes. In J. M. Atkinson & J. Heritage, J. M. Atkinson & J. Heritage (Eds.), Structures of social action: Studies in conversation analysis (pp. 57–102). Cambridge: Cambridge University Press.
Schütz, A. (1951). Making music together: A study in social relationship. Social Research, 18(1), 76–97.
Stivers, T., & Rossano, F. (2010). Mobilizing Response. Research on Language & Social Interaction, 43(1), 3–31. https://doi.org/10.1080/08351810903471258
Thompson, S. A., Fox, B. A., & Couper-Kuhlen, E. (2015). Grammar in Everyday Talk: Building Responsive Actions. Cambridge: Cambridge University Press,.
Wiggins, S., & Potter, J. (2003). Attitudes and evaluative practices: Category vs. item and subjective vs. objective constructions in everyday food assessments. British Journal of Social Psychology, 42(4), 513–531. https://doi.org/10.1348/014466603322595257
Weeks, P. (1996). Synchrony lost, synchrony regained: The achievement of musical co-ordination. Human Studies, 19(2), 199–228. https://doi.org/10.1007/BF00131494
Wittgenstein, L. (1967). Lectures and conversations on Aesthetics, Psychology and Religious Belief. (C. Barrett, C. Barrett, Ed.). Berkeley and Los Angeles: University of California Press.
In 2015 I wrote a quick introduction to CA for people who study the psychology of interaction and discourse (still in press). I’ve used that as the basis of this longish post for people who need some starting points in #EM/CA. Also check the EMCA wiki for new references (there are more all the time – it’s a flourishing field).
Conversation Analysis (CA) is an interdisciplinary, inductive approach to studying talk and interaction ‘in the wild’ and in situations where the formal parameters, theories and models for interaction are unknown, premature, or where theories are currently undergoing revision.
Conversation Analysis (CA) is a method of gathering data involving naturalistic conversational interaction, analysing it systematically, and reporting on features of its structural organisation. CA is distinctive because it is not only a method for analysis, it also constitutes an active sub-discipline within many research areas that involve the empirical study of human interaction. CA has its own standards of evidence, some unusual collaborative research practices, and a rich literature spanning sociology, linguistics, anthropology, psychology, and communications. The outline of CA provided here should be used as a guide to contextualise the kinds of claims, arguments, and evidence readers may encounter in the CA literature. Because CA has not developed from a ‘home discipline’ as such, it is widely dispersed and consequently likely that any researcher interested in spoken discourse will find a wealth of CA research within their area of specialism. The intention here is to encourage researchers to draw on core CA findings in their work, to find the CA research and researchers in their own field, and to learn to work with interaction data using these methods
So what is CA useful for? What kinds of questions can one ask with it? And what kinds of answers can be gleaned at different points in the research cycle? CA is especially useful for empirical research on interaction in naturalistic settings where established theories may be lacking or under revision. This is because CA looks for detailed qualitative evidence of how participants work to organise their interactions sequentially in each specific situation. CA relies on a recorded event, utterance or gesture as analytic evidence only when the participants demonstrably use that event to organise their subsequent actions. On the one hand, this forces analysts to limit the generality of the questions they can ask and the claims they can make. For example, studies of interaction in doctor’s offices, courtrooms, or at dinner parties tend to ask questions about how a specific action or utterance is produced in a particular social situation by specific participants. On the other hand, CA’s evidential constraints have led to a methodologically coherent field. By focusing analysis on the methods and events demonstrably used by participants to make sense of their own interactions, CA studies tend to be readily comparable with one another. Although individual studies are situationally specific, analysts can develop and test general findings cumulatively working in diverse settings and fields. Over the last 40 years the most robust and broadly tested finding on which much latter CA research has been based is the turn-taking system described by Sacks, Schegloff, and Jefferson (1974). Without the extended discussion these warrant, the rules of the turn-taking system can be summarised briefly to explain what kind of answers CA can offer.
For any turn at talk, at the first possible completion,
current speaker may select next,
next speaker may self-select,
current speaker may continue.
If 1c occurs, the rules are re-applied at the next possible completion.
This describes the normative patterns observed in natural conversational turn-taking across contexts in the first decade of CA research. As a finding it provides a framework for further exploratory work in CA, and a strong empirical basis for theory formation for experimentation. As a research outcome, this exemplifies how CA can produce detailed, systematic descriptions from cumulative observations.
Alongside these longer-term results, the CA research cycle involves structured observation throughout the process of data gathering, presentation, and collaborative analysis of data within the scope of a single study. Current best practice for CA data gathering involves video of an interactional situation from multiple angles where all participants’ gaze direction, gestures, body orientation, and talk are – ideally – available for analysis. Within relevant practical, social, and ethical constraints, it is useful to record whatever participants evidently pay attention to within the setting including objects, tools, documents, computer, phone, and screen captures. Interaction mediated via text, audio, and video also constitutes viable data, however for a sequential analysis, participants and CA researchers should be able to access the same evidential and temporal contingencies and constraints. For example, phone calls provide ideal data for CA studies because participants and researchers alike can analyse the same audio events in the same order. Because a CA study may focus on very intricate details, a few seconds of a recording can yield data for a ‘single case analysis’, contributing to or questioning cumulative findings. Researchers also re-analyse data from previous studies, use examples from audiovisual corpora and data fragments from the CA literature, often as a foil for discussion.
Transcription is central to CA research as it involves repeatedly reviewing the data to build up an initial description that can be checked by others from an early stage. Variations on Gail Jefferson’s transcription conventions1 provide a level of detail that can be adjusted for the specific phenomena in question. Verbal interaction is typed out turn-by-turn, then symbols are added and arranged spatially to indicate temporal and production features of talk. For example, extract 1 depicts Paul and Anne’s talk as their teacher sings a count of eight during a partner dance class. Links to online data are also provided where possible.
Extract 1 CADANCE: eg1
1 Paul: N↑o⌈t b̲a̲::d, >°°not ba:d.°°<⌉
2 Anne: ⌊ It's like be⌈ing GENuin⌋ely >able °to do it?°<⌉
3 Tchr: ⌊ F I v e ⌋, (.)
4 s⌈ix? ( . ) ⌉ ↓five
5 Paul: ⌊Ye̲::⌈p. ⌋
6 Anne: ⌊∙hh ⌈aHhh ∙Hhh
7 Tchr: ⌊six se::ven eight?
Reading while listening to the audio should show how Jefferson’s conventions are roughly intuitive : left and right braces show points of overlap, carats show talk speeding up, while colons indicate sound stretches. Because these conventions compromise between precision, detail, and readability there are also some inevitable ambiguities, for example punctuation indicates intonation rather than grammar, and turn-initial capitals mark transcriber hearings of turn-beginnings, but elsewhere they indicate loud talk. The purpose, however, is not analysis of the transcript. Rather transcripts provide a useful sketch to aid in more formal description, and a convenient way for analysts to refer to specific moments of the original video in a data session presentation.
In a data session, a researcher presents new data and transcripts for repeated viewings and extended analytical discussion amongst a small group of colleagues. Since CA relies on the linguistic and interactional aptitude of the analyst as an heuristic instrument, regular data sessions provide an essential opportunity to revise transcripts and candidate analyses amongst peers. Details of the present data are discussed in relation to cumulative findings, and the implications of or alternatives to each analysis are proposed and challenged. Ideally, data sessions are both pedagogical and deliberative, where experienced and student analysts refine their observations and descriptions by picking out specific fragments of data, and contextualise findings within the literature. Over time, researchers build ‘collections’ of data fragments such as extract 1: part of a collection of ‘countings’, where people count up or down to coordinate joint actions. A rough collection is a starting point for identifying a distinct social practice as a specifiable analytic phenomenon. Analysis then refines a collection in terms of how participants orient to the sequential organisation of an action, and to its lexical, grammatical, and/or embodied structural features of composition and design (Schegloff 1993, 121). For example, before the video clip of extract 1 starts, Paul and Anne have been evaluating their previous attempt at a dance move. The teacher’s count starts with a loud, stretched “”, a short pause then a rising “”, before both pitch and count re-sets to five and moves back up to a final, rising “”. At the onset of the count, Paul’s turns his head to the teachers and back to Anne, hushing his second “”. Anne also speeds up and softens her talk, turning her head towards the teachers then back to Paul as the count reaches its first “”. Paul’s minimal “” receipts Anne’s assessment just as he briefly turns his head away from her again. Her laugh closes the sequence, and they re-establish mutual gaze as the count enters its final phase.
Forgoing more detailed description on the one hand, and the broader sequential context on the other, this fragment provides a simple example of how such data can be presented. The embodied turn (Nevile 2015) in the CA literature has led researchers to add more detail to transcripts of talk, using illustrations (Laurier 2014) to describe gesture and gaze direction as well as diagrammatic representations of, for example pitch tracks and phonological details. Figure illustrates the temporal structure of talk and patterns of other-directed mutual gaze just before Paul and Anne start dancing.
Figure 1: Paul and Anne’s pattern of gaze orientation in extract 1
In terms of cumulative CA findings, these details could be analysed alongside generalised CA work on how assessments implicate sequence closure in everyday conversation, and how patterns of mutual gaze work towards topic, focus, and activity shifts (Heath 1986, 128–48; Mondada 2006; Rossano 2012, 227–308). In a more applied project, the way the dancers’ turns at talk and gaze shifts match the phase structure of the teacher’s count could be analysed in relation to ongoing research into how bodily-vocal group activities are organised in dance instruction (Keevallik 2014). This fragment may be added to multiple collections including ’embodied closings’ or ‘countings’ as well as specialised sub-collections such as ‘dance closings’ and ‘count-ins’. CA findings are thus developed incrementally by documenting the detail of people’s interactional practices in specific settings while contributing to a general understanding of ‘everyday talk-in-interaction’. This super-set of copresent interactional practices provides a normative basis for researchers studying specialised settings where institutional or practical constraints may constrain interactional practices (Drew and Heritage 1992) Identifying and fully describing a new phenomenon in these terms may therefore require collection of hundreds of cases, but a single case analysis can still test, discuss, or suggest a finding by demonstrating its use in a specific context.
CA can also be used in mixed-methods research, especially in theory formation, experimental design, and evaluation processes. CA researchers may discover a systematic variation in participants’ situated action, sometimes as simple as an issue of lexical choice. For example, Heritage et al. (2007) observed that doctors vary the ways they ask about patients’ unmet concerns during consultations. Their experiment asked doctors to request whether their patients had “anything else” or “something else” to talk about, and discovered that 78% fewer unmet concerns were reported in the latter condition. In this way CA’s focus on interactional practices in natural settings provides systematic observations that can help design ecologically sound experimental variables and guide the formulation of falsifiable theories (Robinson and Heritage 2014). In conjunction with more conventional social science methods, CA is useful in similar ways when it foregrounds the participants’ interactional uses of the research setting. For example, CA studies of interviewing practices (Potter and Hepburn 2012) contribute to methodological developments that are starting to incorporate the pragmatics of talk and the practicalities of survey technologies into a broader analysis (Conrad, Schober, and Schwarz 2013). Similarly, studies of methods that use introspective self-report (Wooffitt and Holt 2011) or CA’s own practices of video recording (Hazel 2015) are opening up new opportunities to approach theoretical questions across fields as practical, observable issues based on the endogenous organisation of situated activities. CA’s early focus on everyday talk has both influenced and drawn on the interactional respecification of core questions in linguistics and pragmatics (Ochs, Schegloff, and Thompson 1996; Levinson 1983), and psychology (Edwards and Potter 2001; Tileagă and Stokoe 2015), along with a broader shift in the social sciences towards posing empirical questions in terms of practical action (Button 1991; Lynch 1997). To use CA within a broader scientific context, however, it is necessary to clarify how its findings are descriptive of normative structures in talk rather than predictive or prescriptive, and may be combined with other methods in order to develop and test formal hypotheses (Lynch 2000, 522).
Atkinson, J.M., and John Heritage. 1984. Structures of social action: Studies in conversation analysis. Edited by J. Maxwell Atkinson and John Heritage. Cambridge: Cambridge University Press.
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Hutchby, Ian, and Robin Wooffitt. 2008. Conversation Analysis. 2nd Edition. Chichester, U.K.: Polity Press.
Keevallik, Leelo. 2014. “Turn Organization and Bodily-Vocal Demonstrations.” Journal of Pragmatics 65. Elsevier: 103–20. doi:10.1016/j.pragma.2014.01.008.
Laurier, Eric. 2014. “The graphic transcript: Poaching comic book grammar for inscribing the visual, spatial and temporal aspects of action.” Geography Compass 8 (4): 235–48. doi:10.1111/gec3.12123.
Levinson, Stephen C. 1983. Pragmatics. Cambridge: Cambridge University Press.
Lynch, Michael. 1997. Scientific practice and ordinary action: Ethnomethodology and social studies of science. Cambridge: Cambridge University Press.
———. 2000. “The ethnomethodological foundations of conversation analysis.” Text – Interdisciplinary Journal for the Study of Discourse 20 (4): 517–32. doi:10.1515/text.1.2000.20.4.517.
Mondada, Lorenza. 2006. “Participants’ Online Analysis and Multimodal Practices: Projecting the End of the Turn and the Closing of the Sequence.” Discourse Studies 8 (1). SAGE Publications: 117–29. doi:10.1177/1461445606059561.
Nevile, Maurice. 2015. “The Embodied Turn in Research on Language and Social Interaction.” Research on Language and Social Interaction 48 (2): 121–51. doi:10.1080/08351813.2015.1025499.
Ochs, E, Emanuel A Schegloff, and Sandra A Thompson. 1996. Interaction and grammar. Edited by Elinor Ochs, Emanuel A Schegloff, and Sandra A Thompson. 13. Cambridge: Cambridge University Press.
Potter, Jonathan, and Alexa Hepburn. 2012. “Eight Challenges for Interview Researchers.” In The SAGE Handbook of Interview Research: The Complexity of the Craft, edited by Jaber F. Gubrium, James A. Holstein, Amir B. Marvasti, and Karyn D. McKinney, 1st ed., 555–71. SAGE Publications, Inc. doi:10.4135/9781452218403.
Robinson, Jeffrey D., and John Heritage. 2014. “Intervening With Conversation Analysis: The Case of Medicine.” Research on Language and Social Interaction 47 (3): 201–18.
Rossano, Federico. 2012. “Gaze behavior in face-to-face interaction.” PhD thesis, Radboud Universitet Nijmegen.
Sacks, Harvey, Emanuel A Schegloff, and Gail Jefferson. 1974. “A simplest systematics for the organization of turn-taking for conversation.” Language 50 (4): 696–735. doi:10.2307/412243.
Schegloff, Emanuel A. 1993. “Reflections on Quantification in the Study of Conversation.” Research on Language & Social Interaction 26 (1): 99–128. doi:10.1207/s15327973rlsi2601_5.
Schegloff, Emanuel A. 2007. Sequence organization in interaction: Volume 1: A primer in conversation analysis. Cambridge: Cambridge University Press.
Sidnell, Jack. 2010. Conversation Analysis: An Introduction. Oxford: Wiley-Blackwell.
Sidnell, Jack., and Stivers, Tanya. (Eds). 2012. The Handbook of Conversation Analysis. London: Wiley-Blackwell.
Tileagă, Cristian, and Elizabeth Stokoe. 2015. Discursive Psychology: Classic and Contemporary Issues. London: Routledge.
Wooffitt, Robin, and Nicola Holt. 2011. Looking In and Speaking Out. Exeter: Imprint Academic.
Footnotes
1See the basic transcription conventions on Prof. Charles Antaki’s CA tutorial website: http://homepages.lboro.ac.uk/~ssca1/notation.htm or the comprehensive account in Atkinson & Heritage (1984, ix–xvi).
This cheat sheet (PDF version) provides all the symbols you will encounter in Schegloff (2007): a useful reminder while doing an initial sequential analysis of your data. Use with caution, and remember to re-read the last chapter, as well as Schegloff (2005) beforehand. Usages are referenced with example and page numbers.
+ : more of a FPP or SPP i.e.: +F / +S (used in combination with other labels) (7.05, p. 121)1
b : base pair i.e. Fb or Sb
pre : pre-sequence marker
e.g. Fpre or Spre of a pre-expansion sequence (5.32, p. 77, see note 5 p. 27)
can take b and / or numbering for multi-sequence analyses.
ins or i : insert expansion FPPins or SPPins (can take b / numbering). (6.08, p.103 / 6.01, p.105)
insins : nested insert expansions (can be further nested e.g.: insinsins ) (6.17, p.110)
post : post-expansion (p. 27 note 5)
Position-specific markers:
pre-S : a preliminary (e.g. anticipatory account) coming between F and S. (p. 69 ex. 5.19)
preSb : a preliminary to a base sequence (p. 84 ex. 5.38)
SCT : sequence closing third (can be used with numbering, + and design feature labels) (7.03, p.119)
PCM : post-completion musing (7.32, p. 143)
Action labels
Obviously actions can be described in many ways, but Schegloff (2007) only uses these ones:
off : offer (could also be req for requests (10.14, pp 213-214), ass for assessments etc. etc.)2
acc : accept prior action (5.39, p. 85)
rej : reject prior action (5.39, p. 85)
prerej : a pre-rejection (could be used for any action) (5.39, p. 85)
req1 / off2 / acc2 / acc1 : numbered actions for multi/nested-sequence analyses. (5.38, p. 85)
retr : disavowal or retraction of prior action (9.03b, p. 185)
alt : alternative version of prior action (7.50c, pp. 166-167)
again : reissuing a prior action (7.50b, pp. 165-166)
redo : reworking/redoing of a prior action (7.49, pp. 163-164)
add : addition to prior action (7.49, pp. 163-164)
Design feature labels
up : upgrade (7.43, p. 157)
hedge or hdg : hedge (7.50b, pp. 165-166)
agree : agreement with preference (5.32, p. 77)
rev : reversal of preference / type conformity (5.32, p. 77)
cnt : counter (2.01 p.17)
References
Schegloff, E. A. (2005). On integrity in inquiry… of the investigated, not the investigator. Discourse Studies, 7(4-5), 455–480.
Schegloff, E. A. (2007). Sequence organization in interaction: Volume 1: A primer in conversation analysis. Cambridge: Cambridge University Press.
This is rather ambiguously described in passing as: “‘preferred’ or ‘+ {plus]’ second pair parts” (Schegloff 2007 p. 120). However, these are not equivalents but alternatives. Confusingly, the literature does sometimes use the + sign to indicate preference in analytic transcripts. Schegloff (2007) uses it to indicate ‘more’ of an FPP or SPP.↩
NB: When using action labels with a b marker, separate them with a comma for clarity e.g.: Fb, req (10.14, pp. 213-214).↩
A brief account of my experience of ICCA 2014 (the 4th International Conference on Conversation Analysis).
Tips gleaned about how to present interactional data analysis in 20 minues.
What I learned about terminology in the analysis and presentation of CA research.
A little reflection on what ICCA 2014 meant for the origins and future of CA.
Introduction
In The Futurological Congress (1971) by Polish science fiction author Stanislaw Lem the protagonist attends a meeting of 70,000 researchers in the increasingly popular discipline of Futurology. There are so many Futurologists these days that they can’t possibly all give their papers in full, so they are assigned index numbers, then when it’s their turn to present they stand up and say the number. Of course there also isn’t time for questions, so all questions have to be submitted in advance and delivered by the questioners standing up and saying the number. Then the speaker may respond with the index number of their response and so on.
ICCA 2014 at UCLA was not quite at this stage yet, but it was still an awesome experience to see 500 Conversation Analysts and Ethnomethodologists from around the world gathered to present papers to one another in one of nine concurrent sessions, each paper containing a series of line-numbered transcripts of spates of interaction each of which – in themselves – could have been the subject of an entire day’s workshop.
Having watched something like 50 presentations in 4 days, with a vast range of styles and approaches, the purpose of this post is to collate tips and ideas for presentation of this kind of research, as well as provide those who weren’t able to be there with a brief impression of what ICCA 2014 was like to attend.
Overall Impressions of ICCA 2014
Conference opening speech by John Heritage
Firstly, a brief impression, which I hope will dispel any negative implication in my referencing Lem’s sci-fi satire in the introduction. This was an extraordinarily well organised and enjoyable event. The venue, the programme, the facilities, and all the important basics were so well organised, they appeared seamless. Sessions progressed on time and with a cooperative and collegial atmosphere that everyone – especially the heroic session chairs, graduate helpers and local organisers – worked so hard to maintain. Despite its size and the diversity of approaches to the study of human interaction, my overall sense was that this conference served an unusually cohesive research community with a strong set of methodological and philosophical alignments. This became most evident to me when I realized – after fretting over having to hop between the 9 concurrent sessions to catch everything I wanted to see – that I could just as well stay put in one session, or walk into almost any other one and still find something interesting and comprehensible to me going on in every room. This is a real contrast with other conferences I’ve attended, especially in a Computer Science context where the widespread intra-field specialization means that walking into the wrong session might result in having to sit and listen to an hour of highly technical niche gibberish.
The panels I enjoyed most used this double aspect of EM/CA’s ethnographic subject-diversity and its methodological coherence to great effect. For example Arnulf Depperman’s excellent sessions on ‘Disjunct and convergent temporalities and the coordination of action’ brought together Jürgen Streeck’s work on the postural configurations of car mechanics at work, Dirk vom Lehn’s work on gallery and museum visiting, as well as Depperman’s own research into driving instruction, and Sae Oshima’s analysis of the interactional dynamics of stylists and clients as they come to the evaluation-relevant endpoint of a haircut. This was one of the most powerful examples to me of how very different interactional contexts and activities, looked at together with different analytic foci, can cohere by allowing a sense of the stable underlying structure of natural human interaction and conversation to emerge from the mix.
Two approaches to presenting (and doing) interaction analysis
Having mentioned ‘methodological coherence’, I should also point out that there were very different approaches and methods too, and they were also presented quite differently. The most obvious differences in overall approach were somewhat similar to the different dimensions of distinction in CA drawn by Emmanuel Schegloff (1996) in his paper on person reference between single case and interaction-oriented analyses on one hand, and aggregate and system-oriented analyses on the other. While these distinctions were not always entirely clear in the 20 minutes most people had to present, it became clear to me that the best presentations were the ones that chose a style to match this aspect of their analytic approach.
Here are some tips based on the best of each type of presentation that I saw. Some are common knowledge or are gleaned along with my reading of the King’s group’s excellent book on video analysis (Heath, Hindmarsh & Luff 2010).
Tips for presenting single case / interaction-oriented analyses
Get to the data almost immediately, show first then tell.
Make the context descriptions as integrated into the presentation as possible, and always illustrated with visible examples.
Make time to show the same clip as many times as possible at multiple speeds.
Don’t sit still – physically demonstrate and illustrate the embodied actions that are being talked about.
Show clips with and without audio, but make sure to use the volume control when speaking over the video otherwise its impossible to hear.
Avoid using transcripts, or if necessary use subtitles to avoid splitting audience focus between page and screen.
If there are transcripts/cartoons/diagrams, show those first, then the video so the audience knows what to look for and gets the satisfaction of seeing it.
Jon Hindmarsh’s presentation was an excellent example of this approach. He showed the same clips on silent repeat while he walked around talking and gesticulating about them animatedly. He also returned to the same clips at the beginning and end of his session, giving us the opportunity to see our perception of the action in the clips changing as we heard his analysis. The final time we saw it at the end was quite powerful for this reason.
Tips for presenting aggregate / system-oriented analyses
Make clips and transcripts as short and concise as possible, just focus on the phenomenon in question.
If there is important stuff earlier or later on in the interaction, show it on screen using subtitles/animation.
Make introductions pithy and descriptive, but don’t spend too long on them. If relationships etc. are important, do a diagram.
When showing an interactional effect, show it not happening too. Standard practices should be demonstrated alongside deviant cases.
When going through the analysis, only talk about details relevant to the phenomenon. A presentation is not the place for a comprehensive analysis, and it’s distracting from the main point.
Be extra careful not to run out of time. Almost all the presentations ran out of time, and whereas I could still get a lot from a badly timed interaction-oriented analysis, it was really hard to make sense of aggregate analyses that never completed their narrative arc.
Don’t show video if the analysis does not depend on it.
Alexa Hepburn and Paul Drew’s paper on absent apologies was a great example of an aggregate analysis of a phenomenon that – by virtue of its absence – required both deviant and non-deviant cases to be presented in a kind of drip-drip of evidence, building up to an aggregate view of the phenomenon. Along the way there were lots of mini-system insights into how the mechanisms of accounting and accountability for apologies work in different ways, and although they did run out of time a bit, they were able to skip several of the deviant cases to complete the overall picture and reach the phenomenon in question. They also had excellent transcripts which included the ethnographic glosses so you could also reconstruct the missing pieces of the puzzle from the data after the fact.
The most important thing I learned at ICCA 2014
In Lem’s novel, the academic discipline of futurology is based on a practical extrapolation of Sapir-Whorfian notions of language being a necessary prerequisite for rendering the world intelligible through thought. The practice of futurology involves future-casting by a kind of reverse-etymology. Futurologists come up with new words and phrases and evaluate them for their potential meaningfulness. The idea is that if your phrase is semantically loaded and suggestive of other, related terminologies, it may at some point come to mean something. The implications of those potential meanings are then the concern of futurologists.
Alongside a fantastic series of pre-conference workshops, graduate students were invited to sign up to have lunch with a group of CA grandees. I was thrilled to get (literally) a ticket to talk with Anita Pomerantz – whose work was the starting point for my entire PhD thesis. It was great to meet and thank her in person for that, and very interesting to hear what she had to say when I insisted she give us some sage advice (her own very gracious approach was to focus on us and our research interests). Notably, she said “don’t use the term preference”, and went on to advise against using terms like “adjacency pair” and all the other bits of terminology established analytically by her first generation of CA people.
This intrigued me, as she is often credited with initiating a whole rich seam of work on preference and dispreference with her work on compliment responses (1978) and second assessments (1984). She explained that she was telling us to avoid using these words as shortcuts to, or even replacements for a proper analysis. I have heard similar things from others in her generation, for whom using these terms must feel very different given that they had to do the analytic work first to clarify and then invent them.
As the conference proceeded, I got a very tangible sense of why this advice is so important. Very often I saw people presenting great research, but peppering their presentations with these kinds of keywords, usually needlessly. These terms are useful, especially for structuring training and disciplined analysis, and for spreading knowledge about CA and its inner workings. It’s hard to imagine a CA textbook not including a full description of adjacency pairs or preference organization. However, their use in this context mostly seemed aimed at expressing group membership rather than contributing to the analysis at hand.
So, given that this was my first CA conference, the key thing I learned at ICCA 2014 was that it’s only worth naming these analytic terms in presentations of research findings where they are absolutely salient to the analysis and phenomena at hand. The transcripts and the data are presented so that other researchers who are familiar with CA methods can challenge the conclusions drawn from them. All the CA terminology is a vital but essentially back-room business that doesn’t need to feature in the presentation of findings at all.
The Future(ology) of CA
Given the breadth and diversity of the research presented at ICCA 2014, it’s difficult to sum it up in anything other than a very partial and subjective way. Having said that, there were a few moments and aspects of the event that suggested some interesting potential directions of contemporary CA.
Firstly, there was a set of presentations in the ‘Hybrids Heretics and Converts’ category that had its own panel on the Friday. Unfortunately I missed most of this, but had the chance to speak to some of the presenters and their colleagues – many from the Max Planck Institute for Psycholinguistics in Nijmegen, and most supervised by Stephen Levinson. The presentations I did see had a distinctive style and structure, familiar to me from hypothetico-deductive models of presentation that I see in my home disciplines of Computer Science and Engineering. I was pleased to see Schegloff in these panels, respectfully engaged with (mostly) young researchers, offering constructive critique. One of his criticisms of much of the experimental work presented was that it tended to focus on moments of experimentally-salient (but interactionally isolated) conversational structure. His argument was that talk is densely layered and interconnected, and that by isolating temporal fragments of talk from continuous processes of interaction, all that contextual interconnection is lost. It was great that there was space for debate focussed on these issues that extend from long-running questions of quantification in the study of human interaction, which is especially healthy given the quantitative (though not yet widespread experimental) turn in recent prominent CA work.
Photo of Harvey Sacks from the exhibition ‘Order at all points: the work of Harvey Sacks’
Secondly, the ISCA general meeting was interesting for me, as a neophyte, to find out more about CA as an organisational project, and get a feel for its origins and future. It was lovely to see Schegloff being awarded an honour for lifetime achievement, and the speeches and presentations were moving, especially given that this is the 40 year anniversary of what is often seen as the founding of the discipline with Sacks Schegloff and Jefferson’s 1974 turn-taking paper. There was also a fantastic exhibition entitled “Order at All Points: The Work of Harvey Sacks” in the Young library featuring key papers, letters, photos and artifacts from the Sacks archive. Alongside these celebrations, ISCA chair John Heritage set out that the focus of the next four years would be on developing and disseminating more educational resources in CA – and he announced that Schegloff was very generously donating all his transcripts, course notes, assignments and recordings to ISCA for publication. While many people whooped and cheered at this bit, Schegloff turned around in his seat at the front and hooted “Yo:::u’ll be S^OrE:::e,” at the crowd.
Video still from the exhibition ‘Order at all points: the work of Harvey Sacks’
Finally, the ICCA baton was passed from ICCA 2014 lead organiser Tanya Stivers to conference chair Paul Drew at Loughborough where the 2017 conference will take place, with Lorenza Mondada also announcing that there would be an interim ISCA-sponsored conference in Basel in 2015 entitled ‘Revisiting Participation’. Given how much I enjoyed and got out of ICCA 2014, I’m very much looking forward to those.
References
Heath, C., Hindmarsh, J., & Luff, P. (2010). Video in qualitative research: analysing social interaction in everyday life. Sage Publications.
Lem, S. (1985). The Futurological Congress (from the Memoirs of Ijon Tichy). Harcourt Brace Jovanovich.
Schegloff, E. A. (1996). Some Practices for Referring to Persons in Talk-in-Interaction: A Partial Sketch of a Systematics. In B. Fox (Ed.), Studies in Anaphora (pp. 437–85). Amsterdam: John Benjamins Publishing Company.
Pomerantz, A. (1978). Compliment responses: Notes on the co-operation of multiple constraints. In J. Schenkein (Ed.), Studies in the organization of conversational interaction. Academic Press.
Pomerantz, A. (1984). Agreeing and disagreeing with assessments : some features of preferred / dispreferred turn shapes. In J. M. Atkinson (Ed.), Structures of social action: Studies in Conversation Analysis (pp. 57–101). London: Cambridge University Press.
The ‘data session’ has become my favourite research activity since starting to work with ethnomethodology (EM) and conversation analysis (CA). However, this crucial bit of analytic trade-craft seems poorly documented as a research process – with minimal references scattered throughout textbooks, articles and course materials. This post pulls together some of the descriptions and tips I’ve found relating to the practical activity of doing data sessions, followed by a short account of why I am so fond of this wonderful research practice.
Early CA work
I can’t find any direct references to data session practices in any of the early CA literature from Sacks, Schegloff or Jefferson. However, there are some very interesting methodological discussions that provide insight into how data is prepared and collected prior to a principled collection being established in the following two papers:
I can only assume that the trade craft of CA was being established at this time, so the practice of the data session was not yet at a point where it was stable, well understood and ready to be written up for instructional purposes. I’ve heard stories (but can’t find any write-ups) of how Gail Jefferson was particularly involved in its development as a pedagogical/analytic practice. I would be very interested in reading these stories – and particularly learning about any rules / procedures for doing data sessions that may have been established in these early days.
First instructional descriptions
Paul ten Have’s “Doing Conversation Analysis” first published in 1999 provides one of the first instructional descriptions of the data session I can find – and lays out the essentials of what the data session consists of very clearly:
“The data session can be seen both as a kind of playground to mutually inspire one’s understanding of the data, and as an environment that requires a rather specific ‘discipline’. A ‘data session’ is an informal get-together of researchers in order to discuss some ‘data’ – recordings and transcripts. The group may consist of a more or less permanent coalition of people working together on a project or in related projects, or an ad hoc meeting of independent researchers. The basic procedure is that one member brings in the data, for the session as a whole or for a substantial part of it.”
He then provides – as far as I can find – the first description of the actual practical activity in the data session, and how it functions as a pedagogical as well as an analytic practice:
“This often involves playing (a part of) a tape recording and distributing a transcript, or sometimes only giving a transcript. The session starts with a period of seeing/hearing and/or reading the data, sometimes preceded by the provision of some background information by the ‘owner’ of the data. Then the participants are invited to proffer some observations on the data, to select an episode which they find ‘interesting’ for whatever reason, and formulate their understanding, or puzzlement, regarding that episode. Then anyone can come in to react to these remarks, offering alternative, raising doubts, or whatever. What is most important in these discussions is that the participants are, on the one hand, free to bring in anything they like, but, on the other hand, required to ground their observations in the data at hand, although they may also support them with reference to their own data-based findings or those published in the literature. One often gets, then, a kind of mixture, or coming together, of substantial observations, methodological discussions, and also theoretical points. Data sessions are an excellent setting for learning the craft of CA, as when novices, after having mastered some of the basic methodological and theoretical ideas, can participate in data sessions with more experienced CA researchers. I would probably never have become a CA practitioner if I had not had the opportunity to participate in data sessions with Manny Schegloff and Gail Jefferson.”
He also mentions that these sessions are poorly documented, writing (in the 1999 and 2007 editions of his book) that he can only find one real description in Jordan & Henderson (1995) quoted below. They also note that the data session – which they call the “Interaction Analysis Laboratory” is both vitally important, and difficult to describe in formal/procedural terms:
“Group work is also essential for incorporating novices because Interaction Analysis is difficult to describe and is best learned by doing. Much in the manner of apprentices, newcomers are gradually socialized into an ongoing community of practice in which they increasingly participate in the work of analysis, theorizing, and constructing appropriate representations of the activities studied.”
They also provide a great description of the actual mechanics of presenting data, and how specific heuristics in the organisation of the data session can mitigate against rambling, ungrounded theoretical speculation:
The tape is played with one person, usually the owner, at the controls. It is stopped whenever a participant finds something worthy of remark. Group members propose observations and hypotheses about the activity on the tape, searching for specific distinguishing practices within a particular domain or for identifiable regularities in the interactions observed. Proposed hypotheses must be of the kind for which the tape in question (or some related tape) could provide confirming or disconfirming evidence. The idea is to ground assertions about what is happening on the tape in the materials at hand. To escape the ever-present temptation to engage in ungrounded speculation, some groups have imposed a rule that a tape cannot be stopped for more than 5 min. This means in practice that rambling group discussions are discouraged and that no single participant can speculate for very long without being called upon to ground her or his argument in the empirical evidence, that is to say, in renewed recourse to the tape.
More recent instructional publications have included tips on data sessions. For example, in Heath, Hindmarsh & Luff (2010), there is one tip section, and one appendix on data sessions. They introduce the basic idea, and also mention its pedagogic function, as well as highlighting the opportunity for its use in interdisciplinary / workplace studies that involve practitioners from other fields:
“[Data sessions] can also be used to introduce new members of a team or research group into a particular project and can be very important for training students in video based analysis. On occasions it can also be helpful to have ‘practitioners’, personnel from the research domain, participate in data sessions, as they can provide distinctive insights, and can often help to clarify events that have proved difficult to understand. in data sessions it is important to avoid overwhelming participants with too much material. A small number of brief extracts of, say, no more than 20 seconds or so is fine. it is also helpful to provide transcripts of the talk as well as any other materials that may be useful for understanding the extracts in question.”
They also add a number of key points about the distinct benefits and caveats of running data sessions, paraphrased here:
identifying candidate phenomena for more detailed study.
Enforcing evidential demonstration of analytic claims.
Revealing issues/challenges in demonstrating analytic findings.
Eliciting alternative/complimentary perspectives.
Generating new analytic ideas/issues and suggesting improvements for future data collection.
Keeping one’s ‘hand in’, i.e. practising analysis on other people’s data to maintain a fresh eye/ear for your own research.
Then they say something explicit about the data session as a collaborative practice which I haven’t seen anyone else mention, but it seems absolutely crucial to me. The fact that this almost never comes up also reinforces my sense that EM/CA and its research practices in general are much less fraught by this particular problem than many other research contexts, which speaks extremely well for it as a community and its empirical/epistemic commitments in general:
“Data sessions are a collegial activity and are based on mutual trust. They should be treated as such and discussions of intellectual Property and the like should be avoided. It is up to individual participants to reveal or withhold ideas that they have, if they do or do not want others use those ideas in future analytic work.”
The appendix with more tips on data sessions (pp.156-157) has more very useful practical advice. To paraphrase:
Limit the numbers to no more than 20 or so.
Presenters should select 3-6 clips, ideally under 30s each.
Do bring transcripts – even rough ones are helpful.
Bring any supplementary material that is relevant/necessary for understanding the action.
Look at one fragment of data at a time – approximate ratio of 20-30m on each 5s of recording.
Don’t cheat and look ahead, or rely on analyst’s information exogenous to the clip itself.
When it’s done, sum up, take notes and get general reflections.
There is a wonderfully reflexive EM/CA analysis of a data session in a chapter by Harris, Theobold, Danby, Reynolds & Rintel (2012) in a volume on postgraduate pedagogical practices that presents the analysis of a data session by the authors and data session participants themselves. They focus on the collaborative / peer pedagogical aspects of the session, and highlight the “fluidity of ownership of ‘noticing'” with reference to clear evidence of how these noticings can be done in this way.
Harris, J., Theobald, M. A., Danby, S. J., Reynolds, E., & Rintel, S. (2012). “What’s going on here?” The pedagogy of a data analysis session. In A. Lee & S. J. Danby (Eds.), Reshaping doctoral education: International Approaches and Pedagogies (pp. 83–96). London: Routledge.
A participant-observer account
Finally, my favourite description of work practices in the data session was written by John Hindmarsh in the affectionate and humorous Festschrift publication he and his colleagues edited for Christian Heath. In an uncharacteristically participant-observer style, he nonetheless describes the detail of both pedagogical and analytic processes of “Heath’s natural habitat: The data session” very vividly. He includes:
Delicate interrogations: where researchers are subtly probed as to why they selected specific clips.
Occasioned exclamations: in which the seasoned analyst will hoot with infectious laughter or joy at a clip – infectious partly because it can leave less experienced researchers either shamefully nonplussed or scrambling to find a grounding for the source of the laughter.
Transcription timings: opportunities to (delicately) rectify transcription errors.
Re-characterisations: moments where a banal, if well-targeted observation is picked up and re-packaged as an elegant and insightful analysis – a form of agreement with some extra pedagogical/analytic impetus.
Troubled re-characterisations: same as above, but done as an (initially veiled) disagreement, demonstrating poor targeting or a flawed analysis – again, always analytically useful and instructive, but less pleasantly so.
Finally – some of my own reflections on the data session – and why it constitutes such an important methodological and pedagogical practice.
Why I love data sessions and why you should too
The last description of the trade craft of a particular researcher’s data session is my favourite because it shows what an excellent apprenticeship situation this is. Whereas instruction in environments where empirical data is less straight-forwardly ready-at-hand, there is a latency between the teaching moment and the understanding moment that is frustratingly difficult to bridge. In this situation, the data is really doing the teaching, but the skilled analyst elicits both the observation and its pedagogical thrust from the same few seconds of interaction that has been in plain sight all along.
Furthermore, this public availability of the data as a mutually assessable resource to the group provides a constant check on authoritative hubris. More than once I’ve seen a junior analyst grasping and holding onto a powerful observation that provides irrefutable counter-evidence to a more experienced analyst’s position on some piece of data. There is honesty and accountability that flows in each direction in the data session, which is what makes it such a wonderful occasion for learning, analysis and – literally – serious fun.
I also like Jon Hindmarsh’s description because it really captures what it’s like to attend data sessions with different people who love the practice. I’m new to it, but thanks to the generosity of my supervisor Pat Healey and his enthusiasm for this work I’ve had the great pleasure of analysing data with pros such as Steven Clayman, Chuck Goodwin, Christian Heath, John Heritage, Yuri Hosoda, Shimako Iwasaki, Celia Kitzinger, Dirk vom Lehn, Gene Lerner, Rose McCabe, Tanya Stivers, Liz Stokoe and Sandy Thompson, not to mention my fellow students in these sessions from whom – given the peer pedagogical structure of the data session – I was able to learn just as much.
My experience has been that everyone approaches data very differently, and each person has a very distinctive style, analytic focus and approach. Nonetheless, the dynamics and epistemic arrangements of the situation allows for an amazingly rich exchange of ideas and empirical observations between disciplines, across interactional contexts, cultures, languages and focal phenomena. I am convinced that it is one of the most crucial factors in how EM/CA projects have made such robust findings in studies of interaction, language and culture, and that there is a great deal more to be understood and appreciated about how they function.
I am also convinced that the data session has a very important place in disseminating EM/CA findings and practices beyond studies that centre onits traditional sociological/anthropological/linguistic contexts of study. There are sure to be ways of adapting some of its pedagogical/analytical dynamism to working with other contexts and types of recorded materials – although it’s debatable whether this form of analysis would really work with anything other than interactional data. In any case, as I mentioned at the beginning – I am very curious about other data session practices and would like to know more about the similarities and differences in how people run theirs, so, I would be very grateful if you would send me your data session experiences/tips/formats and training materials.
At the inaugural EMCA Doctoral Network meeting (my write-up here), where there were a mix of researchers with different levels of familiarity with EM/CA, I realised that the process of preparing for a data session – one of the most productive and essential tools of interaction analysis – is really poorly documented. There are some guidelines provided in textbooks and on websites that usually include issues of how to do the analysis/transcription itself, but nowhere is there a simple guide for how to actually get ready to contribute your data to a data session. This short primer is intended to fulfil that function, and invite others to contribute their own tips and best practices.
I was more or less in this situation (having crammed my head full of CA literature without having had a great deal of hands-on data session practice) last year when I went to the Centre for Language Interaction and Culture at UCLA. There I had the chance to witness and participate in four separate weekly data sessions run by Steven Clayman, Chuck Goodwin, John Heritage, Gene Lerner, Tanya Stivers and Sandy Thompson and their students. It was a bit of a baptism of fire, but I learned a lot from it.
Each of the pros had interestingly different approaches to preparing data for the sessions, all useful in slightly different ways so I decided to write up my synthesis of best practice for preparing your data for a data session. Feedback and comments are very welcome!
What/who this guide is for:
This guide is intended for researchers interested in participating in data sessions in the tradition of Ethnomethodology and Conversation Analysis (EM/CA), who already have data and want to figure out how to present it.
This is not about gathering data, specific analytic approaches or about actually doing detailed analysis or any of the meat and potatoes of EM/CA work which is amply covered in many books and articles including:
This guide is intended to help researchers who may not have had much experience of data sessions to prepare their data in such a way that the session will be fun and analytically useful for them and everyone else who attends.
This is also not intended to be a primer in the use of specific bits of audio/video/text editing or transcription software – there are so many out there, I will recommend some that are freely available but pretty much any will do. I do plan to do this kind of guide, but that’s not what this article is for.
Selecting from your data for the session
Doing a data session obviously requires that some kind of data selection has to be made, so it helps to have a focal phenomenon of some sort. Since the data session is exploratory rather than about findings, it doesn’t really matter what the phenomenon is.
That’s the great thing about naturally occurring data – you might not find what you’re looking for, but you will find something analytically interesting. Negative findings about your focal phenomenon are also useful – i.e. you might find out that you’ve selected your clips with some assumptions about how they are related – you might find that this is not borne out by the interaction analysis. That is still a useful finding and will make for a fun and interesting data session.
Example phenomena for a rough data-session-like collection of extracts might focus on any one or on a combination of lexical, gestural, sequential, pragmatic, contextual, topical etc. features. E.g.:
Different sequential or pragmatic uses of specific objects such as ‘Oh’, ‘wow’ or ‘maybe’.
Body orientation shifts or specific patterns of these shifts during face-to-face interaction.
Word repeats by speaker and/or recipient at different sequential locations in talk.
Extracts from interactions in a particular physical location or within a specific institutional context.
Extracts of talk-in-interaction where speakers topicalize something specific (e.g.: doctors/teapots/religion/traffic).
At this stage your data doesn’t have to be organised into a principled ‘collection’ as such. Having cases that are ostensibly the same or similar, and then finding out how they are different is a tried and tested way of finding out what phenomenon you are actually dealing with in EM/CA terms.
There are wonderful accounts of this data-selection / phenomenon discovery process with notes and caveats about some of the practical and theoretical consequences of data selection in these two papers:
Pre-data session selection: how to focus your selection on a specific phenomenon.
You can bring any natural data at all to a session and it will be useful as long as it’s prepared reasonably well. However if you want the session to focus on something relevant to your overall project, it is helpful to think about what kind of analysis will be taking place in the session in relation to your candidate phenomenon and select clips accordingly.
There are proper descriptions of how to actually do this detailed interaction analysis in the references linked above. However, here is a paraphrase of some of the simple tips on data analysis that Gene Lerner and Sandy Thompson give when introducing an interdisciplinary data session where many people are doing it for the first time in their wonderful Language and the Body course:
Describe the occasion and current situation being observed (where/when/sequence/location etc.).
Limit your observations to those things you can actually point to on the screen/transcript.
Then, pick out for data analysis features/occasions that are demonstrably oriented to by the participant themselves.
That is your ‘target’, then zoom in to line-by-line, action-by-action sequences and describe each.
Select a few targets where you can specify what is being done as the sequence of action unfolds.
Then in the data session itself, you and other researchers can look at how all interactional resources (bodily movements / prosody / speech / environmental factors) etc. are involved in these processes and make observations about how these things are being done.
Providing a transcript
I find it very hard to focus on analysis without having a printed transcript but there are a few different approaches each with different advantages and disadvantages. Chuck Goodwin, for example, recommends putting Jeffersonian transcription subtitles directly onto the video/audio clips so you don’t have to split focus between screen and page. However, most researchers produce a transcript using Jeffersonian transcription and play their clips separately.
Advantages of printed transcriptions
You and other participants have something convenient to write notes on.
You can capture errors or issues in the transcription easily.
Participants can refer to line numbers that are off-screen when they make their observations.
Advantages of subtitles on-screen
You don’t miss the action looking up and down between page and screen.
Generally easier to understand immediately than multi-line transcript when presenting data in a language your session participants might not understand.
You can present this data in environments where you don’t have the opportunity to print out and distribute paper transcripts.
In either case you will need to take the time to do a Jeffersonian transcription so why not do both?
Jeffersonian transcription
There are lots of resources for learning Jeffersonian transcription, here are some especially useful ones:
Ochs, Elinor (1979) Transcription as theory. In: E. Ochs and B.B. Schiefelin, eds. Developmental Pragmatics. New York: Academic Press: 43-72
Hepburn, A., & Bolden, G. B. (2013). The Conversation Analytic Approach to Transcription. In J. Sidnell & T. Stivers (Eds.), The Handbook of Conversation Analysis (pp. 57–76). London: Wiley-Blackwell.
Chuck and Candy Goodwin often also present carefully designed illustrations alongside their final analyses. Some people also present their data, usually at a later stage of research with detailed multi-modal transcripts incorporating drawings, animations, film-strip-like representations etc. (see Eric Laurier’s paper for a great recent overview):
Laurier, E. (2014). The Graphic Transcript: Poaching Comic Book Grammar for Inscribing the Visual, Spatial and Temporal Aspects of Action. Geography Compass, 8(4), 235–248.
How much work you want to do on your transcript before a data session is up to you but it is probably premature to work on illustrations etc. until you have some analytic findings to illustrate.
Transcription issues vs. errors
It’s inevitable that other people will hear things differently, so the data session is a legitimate environment for improving a transcript-in-progress. In fact, often analytic findings may hinge on how something is heard, and then how it is transcribed – this is a useful thing to discuss in a data session and will be instructive for everyone. However, it is important to capture as much as possible of the obvious stuff as accurately as possible to provide people with a basic resource for doing analysis together without getting hung up on simple transcription errors rather than the interesting transcription questions.
Introducing your data
It is useful to give people a background to your data before you present it. This does not have to be a full background to all your research and the study you are undertaking. In fact, it’s useful to omit most of this kind of information because the resource you have access to in the data session are fresh eyes and ears that aren’t yet contaminated by assumptions about what is going on.
In terms of introducing your study as a whole, it’s useful to have a mini presentation (5 mins max for a 1.5h data session) prepared about your study with two or three key points that can give people an insight into where/what you are studying. Once you’ve made one of these for each study you can re-use it in multiple data sessions.
In terms of introducing each clip, have a look at Schegloff’s (2007) descriptions of his data extracts. They have a brilliantly pithy clarity that provides just enough information to understand what is going on without giving away any spoilers or showing any bias.
Preparing your audio/video data.
Assuming you already have naturalistic audio/video data of some kind, make some short clips using your favourite piece of audio/video editing software. The shorter the better (under 30s ideally) – longer clips, especially complex/busy ones may need to be broken down for analysis into smaller chunks.
It can be time-consuming searching through longer clips for specific sections, so I recommend making clips that correspond precisely to your transcript, but noting down where in the larger video/audio file this clip is located, in case someone wants to see what happens next or previously.
Copy these clips into a separate file or folder on your computer that is specifically for this data session – finding them if they’re buried in your file system can waste time.
If possible, test the audio and video projection/display equipment in the room you’re running the data session to make sure that your clips are audible and visible without headphones and on other screens. If in doubt, use audio editing software (such as Audacity) to make sure the audio in your files is as loud as possible without clipping. You can always turn a loud sound system down – but if your data and the playback system you’re using is too quiet – you’re stuck in the data session without being able to hear anything.
I find it useful to think of this as a kind of presentation – just like at a conference or workshop, so I recommend using presentation software to cue up and organise your clips for display rather than struggling looking through files and folders with different names etc…
Make sure each clip/slide is clearly named and/or numbered to correspond with sections of a transcript, so that people can follow it easily, and make sure you can get the clip to play – and pause/rewind/control it – with a minimum of fuss.
The data session is probably the most useful analytic resource you have after the data itself, so make sure you use every second of it.
Feedback / comments / comparisons very welcome
I hope this blog-post-grade guide is useful for those just getting into EMCA, and while I know that data session conventions vary widely, I hope this represents a sufficiently widely applicable set of recommendations to make sense in most contexts.
In general I am very interested in different data session conventions and would very much welcome tips, advice, recommendations and descriptions of specialized data session practices from other researchers/groups.
Dr Jo Meredith adds: “because data sessions can be a bit terrifying the temptation’s to take some data you can talk about in an intelligent way, best data sessions i’ve been to have been with new pieces of data, and I’ve got inspired by other people’s observations”
