We use 'ums' and 'ers', verbal fillers, (‘y’know’, ‘so’, and ‘like’), and encouragers (‘uhuh’, ‘mmm’). We also interrupt, talk over the other, fail to finish sentences, finish a sentence for the other, pause and use intonation in our speech to communicate. It can seem random and disorderly. But the fact that we can largely comprehend each other suggests that there is an order to this seeming randomness.
When I first came across Conversation Analysis (CA), in John Heritage’s Garfinkel and Ethnomethodology1, its detail and specificity appealed to my scientist brain. And as a qualitative researcher the appeal of grasping the detail of individual words and phrases also appealed, along with apparent script-like talk (being influenced by the likes of Erving Goffman). But I wasn’t entirely grasping the point of what Conversation Analysis is for.
So, what is Conversation Analysis?
According to Paul ten Have (2007)2 in Conversation analysis what an utterance means (or the job that it is doing) depends on its order (or sequential position). Not just in the speech of an individual, but also the speech of others in an interaction. Fundamentally CA is about analysing turn-taking in speech.
Doesn’t sound very exciting or insightful, does it? And isn’t it just dialectics, or social scripting, or even discourse? Yes, but it goes beyond turn-taking in speech or analysis of sequential phrasing. It is also the analysis of sequential positioning of sentences, and the sequential positioning of individual words across that interaction.
The development of Conversation Analysis by Harvey Sack2 in the 1960's was prompted by recordings of telephone calls to a suicide prevention centre. There is a fascinating (and a little chilling) modern example used by Prof Elizabeth Stokoe, in a YouTube vlog, of a call to the emergency services, where a woman’s spoken words indicate that she is ordering a pizza, but her meaning is very different.
What’s amazing about that call is that it doesn’t take long – a couple of turns – for the 911 dispatcher to realise that ‘I’d like a pizza for delivery’, means ‘Please send the police because someone is threatening my life in my home’ .
There is a lot to say about this example, especially around the increasing use of AI in seemingly scripted human interactions, and its limitations. However to keep within the scope of the blog, Conversation Analysis would be used to study the sequential positioning of words and their meaning with and across an interaction. The caller has used her knowledge of two seemingly very different scripts to convey necessary information (her location) and her meaning (I need assistance). The emergency services dispatcher has used their knowledge of an alternative script (ordering pizza) to enable the caller to use specific individual words (yes/no) that do not disrupt her Pizza ordering script, whilst retaining the meanings required of an emergency call script.
What’s Conversation Analysis good for? When should I choose to use it?
Crucially, Conversation Analysis is used to understand the meanings of real language and how people really speak. Whilst CA has usage across many disciplines including anthropology, linguistics and semiotics, organisational psychology, even business and law, CA has its roots in understanding sociological levels of communication. Talk-interaction within organisational - I might stretch as far as saying ‘institutional’- and procedural settings continues to be a focus of CA, although it is not limited to organised settings. Examples of interaction include Board meetings, classroom or instructional talk, court proceedings, and patient-practitioner consultations, or even training scenarios.
These examples illustrate another common focus of CA studies; the examination of power within interactions. For example, in the board room you might call it the power-play of individuals attempting to assert dominance or display co-operation, regardless of the quality of the substance of the ideas and suggestions under discussion. In classroom settings, control, dominance, and resistance might be examined in relation to gender. In health, the purpose can be to examine normative power structures, such as the expectation of compliance of the non-powerful, with the wishes of the powerful e.g. that a patient is expected to take their medication in the way that the pharmacist has told them to, or they will comply with a weight-loss programme because the GP has outlined why it is important and a way to achieve it4. CA can help uncover or develop facilitative interaction patterns which can be applied in the training of health professionals, and potentially address perceptions of power imbalances, or at least not make matters worse5.
The position of the researcher in the gathering of data is at a more objective position than much qualitative research. You are highly unlikely to be gathering the data through interviewing people yourself. You will be examining audio recordings of the interactions of others in real settings. You may be an observer, or be given access to recordings made by others in real encounters. Any encounter that you examine will not have been set-up for the purpose of research; it will be an end in itself, with no account taken of the research needs. E.g. a meeting that you are allow access to and permission to record for research purposes, or recordings that you have been given access and permission to analyse. You are objectively positioned in the data. That said, CA maintains its qualitative heritage, as the reflexive examination of subjective positioning of the researcher in the analysis of the data is expected.
Things to bear in mind
CA has very particular transcript notation to keep track of all the utterances, intonation, pauses, emphases and drawing of breath: learning to even read a CA transcript can be difficult at first. I remember reading a transcript in Paul ten Have’s work (2007)2 and thinking that it looked like a foreign language to me. Actually, some of the transcripts were in a language that is foreign to me, (Dutch) and fact that I didn’t spot this for a long time illustrates just how strange CA can look at first.
This makes transcription time-consuming (and tedious). I recall advice from my own days of training that 1 minute of talk will take about 10 minutes to create an edited transcription (after practice), whereas 1 minute for CA transcript will take about 1 hour, even with experience. If you are part of a team, the decision to use transcription services may be taken out of your hands. If you are doctoral student, do talk to your supervisor. A doctorate is a key time for developing your skills as a researcher, and my personal view is that learning the skills of transcription is time spent with your data, so it's never wasted. However, you will need to allow time to develop your skills and fluency at both transcribing and reading CA, if you decide it suits your research purpose.
Gail Jefferson developed the eponymous transcription system for four decades ago and such is its strength that it remains highly regarded and in widespread use in sequential analyses, especially CA. You can see an example of Jefferson transcript notation here6, with its under-linings, arrows, timed pauses and other forms of notation. Although it was developed for CA, this style of detailed transcription system is used where-ever specific detail of timing, pauses, emphasis and intonation is required e.g. in some forms of Discourse Analysis. It can also be adapted to create a convention or protocol, where the full range of notation is not necessarily required.
The units of analysis and the volume of data required.
The units of analysis in CA are small, with considerable attention to detail required. Paul ten Have gives an example from the work of R.M. Frankel (ten Have, 2007, P.4.2). It is just 3 lines of text and 18 words of an interaction in which a doctor confirms a patient’s supposition that their cancer treatment will leave them infertile. The narrative to analyse the conversation is four-times as long. This should indicate to you the need for careful decision making around the kinds of interactions that you wish to gather or get access to (and you must have an audio record). Otherwise, you’ll be left with burdensome and unrealistic amounts of data to transcribe and analyse, or insufficient data to form an argument. I don’t always recommend a distinct or short pilot phase for data gathering, but I do for CA. It gives you the chance to effectively assess what you need access to, and realistically estimate how long your analysis will take. Don’t be the student who gathers loads of data but leaves themselves no time to analyse it. Less is more.
As ever it will come down to the purpose of your research. Use it when you are interested in examining sequential or turn-taking speech and you are interested in the sequential positioning of individual words across that interaction. And if you need software to help you explore the dense analysis of qualitative data, give Quirkos a go today - it allows you to add an unlimited number of codes or themes onto a single sentence or word, making work much neater than transcripts and highlighters!
1. John Heritage (1984) Garfinkel and Ethnomethodology. Wiley
2. Paul ten Have (2007) Doing Conversation Analysis. A practical Guide. (2nd Ed). Sage. London. P.4.
3. If Data Could Talk: What is Conversation Analysis and Why is it Important? Tableau Software https://www.youtube.com/watch?v=HuxxGqlPIB8&list=WL&index=1&t=52s17:25-18:39.
4. Albury, Charlotte; Webb, Helena; Ziebland, Sue; Aveyard, Paul; Stokoe, Elizabeth (2021): What happens when patients say “no” to offers of referral for weight loss? - Results and recommendations from a conversation analysis of primary care interactions. Loughborough University. Journal contribution. https://hdl.handle.net/2134/16465566.v1 Accessed 12/10/2021
5. Pilnick, A., Trusson, D., Beeke, S. et al. Using conversation analysis to inform role play and simulated interaction in communications skills training for healthcare professionals: identifying avenues for further development through a scoping review. BMC Med Educ 18, 267 (2018). https://doi.org/10.1186/s12909-018-1381-1
6. Jefferson Transcription Example. TP Transcription. https://www.tptranscription.co.uk/jefferson-transcription-example/Accessed 05/10/2021
Word of Mouth on BBC Radio and the 911 pizza call. Real Talk (4 Feb 2020) https://www.bbc.co.uk/programmes/m000dy62 and
Introducing Discourse Analysis for Qualitative Research