Query: Creative ways of collaborative qualitative data analysis

Dear All,

I am reaching out for your insights. The group has set the objectives of the review. they will design the process, collect data, analyse and disseminate. I plan to facilitate online participatory data analysis with a group of about 15-20 staff members from different organisations. The study is qualitative. I have facilitated group data analysis a few times, but would like to go a bit deeper. 

Any creative ideas/experiences on collaborative data analysis would be very welcome! 

Thanks and regards,


Photo courtesy https://www.canstockphoto.com/teamwork-entrepreneurs-engaged-in-811...

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Dear Rituu,

I just tried ChatGPT and it gave me the following response, which I think does capture this well. This can be a starting point, but I'm sure other innovative ways that have been used by practitioners will emerge during the Workshop. 

What are some of the creative ways of collaborative qualitative data analysis?

Collaborative qualitative data analysis involves working with a team of researchers or stakeholders to analyze data and make sense of the findings. There are a variety of creative and innovative ways to approach collaborative qualitative data analysis, some of which include:

  1. Participatory data analysis: This approach involves involving stakeholders in the data analysis process by having them review and interpret the data alongside the research team. This can include holding group discussions or workshops where stakeholders can share their insights and perspectives on the data.

  2. Visual methods: Using visual methods, such as diagrams, maps, or other visual representations, can help to make the data more accessible and easier to understand. These methods can also facilitate collaboration and communication among the research team.

  3. Collaborative coding: Collaborative coding involves working with a team of researchers to identify key themes and patterns in the data. This can involve using coding software that allows team members to code the data independently and then compare and discuss their findings.

  4. Co-creation of categories: Co-creating categories involves involving stakeholders in the development of categories and themes to be used in the data analysis. This can help to ensure that the categories and themes are culturally appropriate and relevant to the stakeholders.

  5. Reflexive analysis: Reflexive analysis involves reflecting on the researchers' own positionality and how this may shape the data analysis. This can involve actively seeking out diverse perspectives and experiences to ensure that the data analysis is inclusive and reflects the complexity of the research topic.

  6. Iterative analysis: Iterative analysis involves conducting multiple rounds of data analysis and review, allowing for ongoing refinement and revision of the analysis. This approach can facilitate collaboration and dialogue among the research team, as well as provide opportunities to check and refine the findings.

Overall, there are many creative and innovative ways to approach collaborative qualitative data analysis, and the best approach will depend on the specific research context and goals.


Hi Rituu,

Thanks for the invite to the discussion

  1. Identify the problem and objective/s of the study where the qualitative data needs are required
  2. Data needs are always related to a particular context of study-population, gender needs, (disaggregated by sex and gender, demographic, etc...). Emanating from evaluation contexts of existing/potential or upcoming research
  3. Build steps 1 and 2 on theoretical/conceptual frameworks to help in answering set evaluation questions.
  4. Link the data sets to steps 1 and 2. Identify whether the participants have their already collected data or if you would like them to work on the data that you already have.
  5. Align the objectives of the participants and yours to reach a common understanding of the training/facilitation.
  6. Brainstorm and decide on the type of analysis that was arrived at earlier during the qualitative data collection phase (open/axial coding, development of preliminary coding, etc)
  7. Decide the type of qualitative analysis after assigning themes and link them again to steps 1, 2, and 3. The type of analysis may be based on content, a thematic, narrative, grounded theory, or discourse analyses), etc.
  8. Prepare and organize the data, review the codes, create initial codes, review the codes, and classify them into thematic areas by revising and combining. (Focusing again on steps 1,2 and 3). Themes should be presented in a cohesive manner.
  9. Do the write-up. Sometimes more qualitative analyses may arise from the data sets and you may end up with more than 1 report/case study depending on the focus.
  10. You may have decided from the beginning whether you would like to do the facilitation with the whole group of participants or divide them into groups (This requires flexibility).
  11. Present the findings to the target audience (e.g. Gender and evaluation forum, donors, and other stakeholders who may be interested in the study).

I hope this is useful based on my understanding.

Thank you, everyone.



Hi Ritu,

There are a few more options to select for this data analysis exercise.


  • Cross-team analysis: Involving individuals from different disciplines, fields, or departments can help bring diverse perspectives to the data analysis process. This can lead to a more holistic and nuanced understanding of the data.

  • Peer review: Have team members review each other's work to check for errors, inconsistencies, or missed insights. This can also be used as a learning opportunity, where team members can provide feedback to each other to improve their analysis skills.

  • Thematic mapping: Visualize data in a thematic map, using software like Atlas.ti, Nvivo, or MAXQDA. This allows team members to see patterns and connections in the data and collaboratively develop a thematic framework for analysis.

  • Interdisciplinary Dialogue: Encourage discussion and dialogue between team members from different disciplines to develop an integrated approach to data analysis. This can lead to a more comprehensive and insightful understanding of the data.

Hello Rittu -

I may have shared with you in the distant past a participatory approach to assessment that I developed while working with the international Red Cross Red Crescent. It was initially called a Participatory Project Review (PPR), but because the method was used for much more than projects, its name has evolved to Participatory Process Review (PPR). It offers a flexible and intuitive approach for participatory assessment that is adaptable to different intervention types, settings, and stakeholders (such diversity in intervention settings was characteristic of RCRC settings). The approach draws upon the tradition of participatory development, critical pedagogy, and participatory monitoring and evaluation (M&E) - especially Most Significant Change technique and Empowerment Evaluation. Here is a 1-page summary of the PPR: PPR%20Brief%202020.pdf.

The PPR process quantifies qualitative data through participatory analysis and ranking, reinforcing stakeholder understanding, prioritization and ownership of findings. It is a heuristic, complexity-adaptive method providing a degree of comparability across time, place, and measurer. Dimensions of inquiry could be tailored towards themes (as discussed below), or the exercise can be used to identify themes, which then are explored more in-depth through other participatory analysis methods. 

Feel free to reach out to discuss separately, an we had discussed over a year ago (my apologies for the slow follow-up) a possible presentation to your outstanding community of practice on a variety of topics, and this very much can be that topic. I believe such complexity adaptative approaches are becoming increasing important amidst the decolonization agenda, as well as the increased frequency and magnitude of disruption that is forcing interventions to explore more nimble ways to assess and adapt interventions. 

Dear all,  I think Collaborative Qualitative Data Analysis is depend on the Issues, Objective, reference Groups and coverage and themes.  I have been working in qualitative evaluation with multi disciplinary theme team and as Sociologist/Anthropologist I used context specific  tools and methods to collect and anlyse the qualitative data analysis.  I mostly used qualitative data analysis tools like priority matrix, wellbeing ranking, pairwise ranking, preference ranking, transects walks, social mapping, resource mapping, events calendar mapping, spider model analysis, root cause analysis, result chain analysis, problem tree analysis, joe harry window, key informant interview focus group discussion, free listing, pile sorting. All these qualitative data collection tools generate and apply based on the what solutions/findings you required. You have to required either scoring, scaling, ranking, coding, weighing and comparison of collected data and before quantified these need to score and coding than it can be analysed. 

Example : if you are analysing qualitative data on  "Social Norms and Gender Preference of Dietary Practices  within a family or homogeneous community than you may use Gender Preferene analysis using pariwise ranking  particular food preference according to family members with relationship  why particular food for particularly person :  son vs daughter, Adult vs child, father vs mother  son in law vs daughter in law etc than you have to score using community perceptions using score/scale number with preference. This type of participatory and collaborative engagement of primary stakeholders (family members or community members --in this context) with learning by entertain and practice collaborative analysis and at last comes with conclusion with justification why .... member of family get ....particular food preference . 

Such collaborative analysis cover only a particular issues, community and comparatively small coverage. There are several tools that required collaborative engagement form designing, developing, implementing and evaluation of particular issues particularly subjective/perceptions/cultural/belief/behavior/opinion/feelings related which cant capture from quantitative methods ............. I have even used in institutional governance capacity assessment, climate resilient capacity or coping capacity assessment using some of above tools.  

Dear Rituu,

Check-out OD insights at https://edcanela.substack.com/p/the-resurgence ... of Conversation Analysis!

Ed is just an awesome fella on data analysis-cum-social-listening in CA: Conversation Analysis.

Kind regards


Bauer S.L., PhD Humanities

D-10999 Berlin 

Hi Rituu,

That sounds a great initiative. I am in agreement with the majority inputs provided by colleagues in here on different methods and approaches for conducting the collaborative qualitative data analysis, especially re the importance for considering that different subject may need different approach.

Just a quick hint, any methods or approaches to be used, setting the context prior to the conduct of the online participatory analysis may worth considering. Including in here is identifying possible personal and cultural bias, especially when it covers different regions, communities/social groups, ethnics. This can be done through asking participants to identify their biases before the event so that you can display the summary matrix table, identify the selected key or influential biases to be further discussed in the first session of your online meeting. You may consider having them in small groups  by pre-identified types of biases, and require each group to further discuss re what do they think about the biases' impact on the result of and how to address the biases in the collaborative analysis. If you use the zoom platform, it can be easily arranged with several different online rooms can be developed with you have the ability to observe the overall rooms. At the end of the discussion, you can always set them back to be in the online plenary room.

All the best for the discussion!


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