Section 9: Participatory data analysis

Timeframe and transparency

Participatory and collaborative data analysis offers significant opportunity to integrate inclusive and transparent data interpretation into the research cycle, and can support the identification of relevant and contextually-informed research findings. However, it is important to consider the timeframe and training necessary for collaborative analysis, and ensure that there is transparency around what will be involved in the process, and the time it is likely to take (Jennings et al., 2018). Paying co-researchers/participants for their involvement in analysis procedures should be a key consideration when designing the research and writing research grants (Vaccaro, 2020).

Key Insights

Make shared decisions about who to involve in the analysis and transcription

Research emphasises the importance of making shared decisions about who will be involved in the analysis (for example, these decisions could be made through negotiation and discussion with participants and/or guided by the reflections of an advisory group of individuals sharing specific characteristics or experiences with the topic in focus). Those deciding who conducts analysis should always ensure that the analysis team represents a diversity of perspectives and expertise, with an emphasis on expertise gained through lived-experience (Jennings et al., 2018). It is also important to think carefully about who could have valuable expertise to be involved in the transcription process.

Researcher Insight:

‘One of our big costs is transcription, getting the transcripts into the *native language of the region*, and then if we translate to English again, it's thousands of pounds... it's important employment for some of the *name of location* team, because they want to be doing that work and be involved. It's work they enjoy, and we trust them because it's also sensitive data, in some cases, that they are transcribing. So that also is an important consideration in terms of just, you want to try to provide employment to local people as well.’ (Researcher 7)

Pre-circulate an analysis guide and session schedule

 It can be really helpful to work with a few members of the team in advance to co-develop a schedule and guide for each analysis session and share this in advance in an accessible format. Jennings and colleagues (2018) suggest this should include the following detail “timings, content and roles (narrative & group dynamic facilitators, data recorders / field note-makers, time keeper)” (p.7)

Ask people confidentially in advance about their preferred way to access data, think, and communicate their perspectives.

Making no assumptions about preferred modes of communicating, processing and reflecting is important, so providing detail of the possible options is useful (e.g., would it be helpful for  someone to read aloud any text/words; would you prefer to hand write ideas, type them or speak them aloud). In this way, access needs can be designed into the session for all to use, without pointing out who needs text read aloud or who can/cannot write. Rix and colleagues (2022) reflected on the diversity of communication preferences in their analysis sessions: ‘These included people using different spoken and signed national languages, people who preferred simplified language and text supported communication, and people who gained access through audio description, braille and through engaging with multisensory objects’ (p.149). Working with maps, post-its, photographs, sketch pads, string and connector magnets can be helpful for facilitating non-verbal reflections.

Book an inclusive space and consult on appropriate timings for the sessions

Ask in advance about physical access needs as well as what breaks and refreshments might help participants feel welcome and able to focus (Jennings et al., 2018). See section on ‘Setting up accessible research spaces’ for further detail and ideas.

Ensure attending the analysis sessions is possible and safe

Vaccaro (2020) describes a ‘low-barrier’ participatory analysis recognising that some of the daily barriers her participants were facing had serious implications for their ability to participate in the analysis: “limited access to shelter, a telephone and a working clock, are merely a few of the contextual realities” (p.7), that impacted on her participants (women experiencing homelessness) ability to participate. 

Key considerations include:

  • Ensure the timing of the sessions (both in terms of date and time of day) can support safe travel to and from where the analysis takes place.
  • Check the sessions do not clash with participants’ personal time-pressures such as the opening and closing of night shelters or foodbanks.

Open each session with a structured and clear process

 Jennings and colleagues (2018) suggest the following:

‘1. Clarify study aims. Explain the PPI co-researcher role, and why their input is valued.

2. Agree role expectations and group ‘ground rules’

3. Undertake warm up, training and reflexive exercises relevant to CDA tasks

4. Use small group / pair work to consider the preliminary framework

5. Bring contributions together, and explore areas of non-consensus with the aim of achieving rationalisation and reconciliation where possible

6. Explore use of language and areas of ambiguity in the preliminary framework

7. Finish with PPI co-researcher feedback exercise.

8. Collate findings (field notes, written / photographed flip chart, feedback)’ (p.7)

Invite reflections and create time for critical thinking

Invite reflections and be careful not to lead or bias discussions: Seal (2021) suggests simple phrases to spark thoughts and discussion: ‘So what is this paragraph saying to us? What does it mean?’ (Podcast excerpt: Seal, 2021).

Develop a basic structure but be careful not to stifle quieter perspectives

Multiple media can be key to ensuring participants can engage with, reflect on and ‘discuss’ the data – use craft materials or digital technology to ensure discussions can include non-verbal interactions. Ensure there is both time for structured thinking as well as unstructured reflection:

‘A semi-structured guide framed a discussion about the significant themes from the interview data, inviting women’s perceptions and considerations of how women’s reoccurring experiences ought to inform responsive housing and social programming for women. Throughout our dialogue, women had access to a range of artistic supplies including paper, markers, a polaroid camera, material for collaging, clay and paint. Making creativity accessible but not putting parameters around how women are expected to engage in this work created a process where women used art to inform brainstorming and reflection’ (Vaccaro, 2020, p.5)

Find an accessible way to discuss analytical rigour and acknowledge bias

‘Support PPI co-researchers to understand that while experience can be used to help interpret the data, all interpretations must have some basis in that data’ (Jennings et al., 2018, p.6). Professor Mike Seal (Seale, 2021) reflects on one participant commenting ‘we just have to make sure we don’t weave our own [s***] into this...we have all had experiences of these things, and we have to be careful of this’.

Explore different types of analysis, validation and interpretation

Different interpretation processes and timeframes may suit different participants and projects:

‘Participant verification and representation of data involved people having an experience, reflecting upon the experience, identifying understandings and insights from that experience, summarising those understandings and insights, recording them and then sharing them with other participants for clarification and verification. The emergent ongoing analysis typically happened soon after an experience, but at times it also took a longer view... Members of one group, for example, were so incensed by their experience at a London Museum, that they spent an afternoon producing a video report where they talked about the access issues that had arisen and then sent it to the museum director’. (Rix et al., 2022, p.147)

Recognise that analysing the experiences of others can be an intense process.

Analysis may bring to light strong and powerful emotions. Be prepared to offer people space or support, and it may be helpful to have a separate room with refreshments where people can take breaks whenever they wish. Some research projects, where trauma is likely to be a key focus, may find it helpful to have a trained counsellor on-hand for both data collection and analysis sessions (Black et al., 2019).

Address conflicting opinions

‘Listen to and explore differences of opinion. When non-consensus occurs, try to create novel synthesis to acknowledge the range of perspectives’ (Jennings et al., 2018, p.6; see also Cornish et al., 2013). Jennings and colleagues (2018) also suggest the use of software to record where perspectives differ and for some methodologies and data sets it may be appropriate to critically reflect on insights derived from traditional processes of inter-rater reliability.

Support participants to feel confident in their strengths and expertise 

Researcher Insight:

In interview, one researcher discussed the importance of creating opportunities for participants to share their expertise and perspectives through whichever medium they feel most confident:

‘With groups of participants who maybe haven't written for a few years, or haven't done these sorts of activities before, to really lean on their strengths and to make it clear that their strengths, and their skills that they have in these areas, are skills that we don't have. And so, to really emphasise that wherever possible, and to direct the activities towards those strengths. Because the participants were very articulate when talking about the quote or what the issue is, but then if it came to writing for example, that would be a bit more difficult, and so we were really trying to lean on those skills...We printed off the reports and were going to try and go through by hand. Then basically, it was much easier for people to engage orally. And so we had data up on a big board being projected, and then we spoke about it as a group and then that was recorded down.’ (Researcher 8)

 Key Literature


  • Black, G., Chambers, M.,  Davies, A. & Lewycka, S. (2019). The Practice and Ethics of Participatory Visual Methods for Community Engagement in Public Health and Health Science.
  • Cornish F., Gillespie A., Zittoun T. (2013) Collaborative analysis of qualitative data. In: Flick U, editor. Handbook of qualitative data analysis. London: Sage; 2013. p. 79–93
  • Flicker, S., Travers, R., Guta, A. et al. (2007) Ethical Dilemmas in Community-Based Participatory Research: Recommendations for Institutional Review Boards. J Urban Health 84, 478–493.
  • Jennings, H., M. Slade, P. Bates, E. Munday, and R. Toney. (2018) “Best Practice Framework for Patient and Public Involvement (PPI) in Collaborative Data Analysis of Qualitative Mental Health Research: Methodology Development and Refinement.” BMC Psychiatry 18 (1).
  • MacLeod, A. (2019). Interpretative phenomenological analysis (IPA) as a tool for participatory research within critical autism studies: A systematic review. Research in Autism Spectrum Disorders, 64, 49-62.
  • Liebenberg, L., Jamal, A., & Ikeda, J. (2020). Extending Youth Voices in a Participatory Thematic Analysis Approach. International Journal of Qualitative Methods, 19.
  • Rix, J., Carrizosa, H. G., Sheehy, K., Seale, J., & Hayhoe, S. (2022). Taking risks to enable participatory data analysis and dissemination: a research note. Qualitative Research, 22(1), 143-153. 1
  • Seal, M. (2021) Mike Seal Interview by and Suzanne Albary Do Research Better Podcast February 2021
  • Tilley, E., Strnadová, I., Ledger, S., Walmsley, J., Loblinzk, J., Christian, P. A., & Arnold, Z. J. (2021). ‘Working together is like a partnership of entangled knowledge’: exploring the sensitivities of doing participatory data analysis with people with learning disabilities. International Journal of Social Research Methodology, 24(5), 567-579.
  • Vaccaro, M. E. (2020). Reflections on ‘doing’ participatory data analysis with women experiencing long-term homelessness. Action Research, 1476750320974429.

Creative Commons Licence This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Cite as: Scott-Barrett*, J., Marshall-Brown*, A., Livingstone-Banks, M., Chrisinger, B., Scher, B., Hickman, M. (2023) Participatory Research: Researcher Insights. University of Oxford *(joint first authorship)