Category Archives: Research Projects

Guest Blog: Using NVivo for CA

Qualitative researchers have an increasing number of digital resources to help them organise their data. Here, Charlotte Albury and Tilly Flint offer a guide to NVivo, a popular and flexible tool for working with focus group data – and one that can be made to work for conversation analysts.

What is Nvivo?

NVivo is a Computer Assisted Qualitative Data Analysis (CAQDAS) software package. It’s commonly used by researchers who work on interview or focus group data to support data management and coding. However, NVivo also can work incredibly well (in fact, rather brilliantly) for conversation analysts to store transcripts, build and manage collections, and begin to make senseof what’s happening in interactional data. Importantly, NVivo does not do any analysis for you – but we’ve found this software provides a workspace and tools that can really support conversation analysis (and conversation analysts). By importing transcripts into NVivo, analysts can organise, edit, and annotate data, making it easy to organise (and reorganise) collections.

What does it look like?

Nvivo’s screen is separated into 3 views. On the far left (Navigation View) you can see an overview of your project, and all the folders in it – it’s like your computer desktop. These folders contain your transcripts (files) and collections (codes). If you select a folder in Navigation View, the contents are shown in the next view over (list view). And when you click on an item in list view it opens up in detail view (far right) showing a particular transcript or collection in detail.

Illustrative Guide

1.   Coding & Coding Stripes

So what Nvivo calls ‘coding’ simply means grouping similar data. In a CA context, we both use the ‘coding’ function to make collections. We would do this by highlighting a piece of data that we want in our collection, this could be a full extract or just a couple of lines. All coded extracts will then be collated under the same code, making it easy to find related extracts. If your collections have multiple components you can organise them under a broader heading, like we do below. You can use as many or as few codes as you wish. This means that you can use NVivo throughout the phases of your analysis. In the early stages of unmotivated looking, you can code all potentially relevant observations, and as the research develops you can turn off the coding for observations that are no longer relevant (or put them in a separate ‘codes’ folder), without losing all of this work.

 Also, when viewing a single transcript, all coding in the extracts will be visible using Coding Stripes (see images below with data from the SBCSAE, see DuBois & Englebretson (n.d.)).

2.   Cases

Cases are a way of grouping data together. This could mean grouping all interactions with the same clinician, the same participants over a period of time in a longitudinal study, or grouping together an interaction with other relevant data, such as survey responses or interview transcripts from the same participant.

3.   Annotations

‘Annotations’ are notes that can be made on transcripts. These annotations can be specific to any amount of highlighted text within a file (as opposed to Memos which link to a whole file). These annotations can be viewed within the file or in the annotations folder.

4.   Memos

Memos are like a blank page in your NVivo file, and be used in whichever way is most useful to you and your research, for example,you could use this space to write analysis notes, keep track of queries and searches, store meta-data or results in the form of graphs or tables, or to record information about the progress of the analysis. Memos can be attached to specific documents in Nvivo they can be stand-alone for broader use.

5.   Searches

Using NVivo’s ‘text search’ function, you can retrieve a particular word, or group of words, supporting accurate and speedy retrieval of data across the whole project. We’ve found this incredibly helpful. To make the most of this function, our tip would be to import both Jeffersonian and verbatim versions of each transcript, as words with notation between letters are not retrieved in searches.

To summarise, NVivo software is an excellent option for conversation analytic work to organise data, manage collections, and annotate transcripts. Here, we have presented a few ways in which NVivo can be used for CA research, however there are many more features that we haven’t discussed here that may be useful when doing interactional research. We hope that by outlining some of the most useful features of NVivo, we will encourage others to make use of NVivo software.

NVivo Resources:

Data Resources:


DuBois, J. & Englebretson, R. (n.d.) SBCSAE Corpus. Accessible at:

Charlotte Albury is a Mildred Blaxter research fellow in conversation analysis and health behaviours, funded by the foundation for the sociology of health and illness, and holds a Fulford JRF at Somerville College, University of Oxford. Her research focusses on advice giving and risk communication in clinical settings, and relationships between in-consultation communication and longer changes to health behaviours. She is course director for Oxford Qualitative Courses, and leads Oxford University’s NVivo courses.

Natalie Flint is a Postdoctoral Research Associate in Clinical Conversation Analysis at the University of Exeter. She is interested in the structural organisation of naturally occurring talk across a variety of settings, namely, clinical encounters, everyday family interactions, initial interactions, and public encounters. Her current research focuses on communicating risk in clinical encounters, and resistance in family interactions.

Studying Video Consultations: How do we record data ethically during COVID-19?

Lockdown in many countries has affected the way in which healthcare workers interact with their patients. In the UK, for example, a number of medical consultations have gone online, with doctors trying to deal with their patients over Zoom or Skype – and it has not been easy. Lucas Seuren has been working in Oxford in a team actively exploring the costs and benefits of online medical consultation, and I’m delighted that he has agreed to send in a report from the front line.

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Lucas Seuren, Oxford University

The outbreak of the COVID-19 pandemic has radically changed the organisation of healthcare services. Social distancing protocols mean that face-to-face contact between patients and health care professionals has to be limited as much as possible. Consultations are now mostly conducted by telephone or video. This provides a unique opportunity for EMCA research on healthcare interaction, but also a significant challenge. Little is still known about how communication works in these remote service models, and as experts on social interaction, we are in a prime position to develop evidence-based guidance. The problem is: how do we get data when we cannot go to places where the interaction take place? Continue reading

Guest blog: Talking with Alexa at home

I imagine that many interaction researchers will have been curious about how a voice-activated internet-connected device might be integrated (or not) into conversations at home.  Martin Porcheron along with Stuart Reeves,  Joel Fischer and Sarah Sharples (all at the University of Nottingham) went the next step, and did the research. Here Martin and Stuart explain how the research was done…

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Martin Porcheron

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Stuart Reeves

Voice-based ‘smartspeaker’ products, such as the Amazon Echo, Google Home, or Apple HomePod have become popular consumer items in the last year or two. These devices are designed for use in the home, and offer a kind of interaction where users may talk to an anthropomorphised ‘intelligent personal assistant’ which responds to things like questions and instructions. Continue reading

Guest blog: Jacob Davidsen and Paul McIlvenny on Experiments with Big Video

How good are your video records? One angle? Two? Wide-angle? Was the camera static or did you move to catch things – and miss other things? How good was the sound? All of us have occasionally been frustrated with what we find on the screen when we come to analyse it, but Jacob Davidsen and Paul McIlvenny have some more fundamental concerns. Just how “big” should data be? 


Paul McIlvenny (l.) and Jacob Davidsen

Continue reading