A colleague and I recently made our first foray into participatory data analysis. We facilitated a Data Party – a gathering of practitioners and community members organized for the purposes of undertaking collaborative data analysis and meaning making.
It was an exhilarating and profoundly engaging experience. There was good food (of course!), a supply of tea, coffee and water to refresh the brain and vocal chords, a format to guide the discussion, plenty of sticky notes and markers, and – most importantly – a wealth of passion, experience and expertise to bring to bear on the data under consideration.
The context for the participatory data analysis was a qualitative evaluation process for a pilot healthcare program in a mixed rural-urban and culturally diverse region of British Columbia. In partnership with the program managers and funders, my colleague and I had developed a process for gathering stories of program impact from patients and healthcare providers. We gathered the stories either in person or over the phone, recorded, transcribed and anonymized them, and then (with the permission of the storytellers) shared them with a group of program stakeholders.
The story package sent to the stakeholders included the following guiding questions for the reader:
- What patterns and themes do you notice?
- Are there any deviations from these patterns?
- What do you find surprising in the stories?
- Does anything in the stories concern you?
- What did you expect to see in the stories that isn’t there?
We facilitated the Data Party/meaning making session using (broadly) the “Focused Conversation” method. This involved the following steps (with each step moving the discussion deeper into the analysis of the data):
Step 1: Objective questions
- What data do we have here? What did you read?
- Whose stories are these?
- What words or phrases stand out for you?
Step 2: Reflective questions
- How do the stories make you feel?
- What surprised you?
- What pleased or delighted or excited you?
- Where did you struggle? What troubled you? What angered you?
Step 3: Interpretive questions
- What themes are apparent?
- What are the most important changes/impacts ascribed to the program? For patients? For professionals? For the system?
- Looking at the impacts, what does this tell you about the value of the program? From what aspect/s of the program does this value derive, in particular?
- What do the stories tell you about the challenges of the program? What underlies these challenges?
- What could be improved? What is needed to make these improvements?
Step 4: Conclusion-building/synthesis questions
- What order of priority do we assign the impacts/changes? (For patients? For professionals? For the system?)
- In addition to the impacts/changes, what else (referring to the group’s analysis) does the future funder (and other stakeholders) need to know?
- What recommendations would you make for the ongoing implementation of the program, based on these data?
- How can the value of the program best be communicated to the future funders? How do you want to use these stories as part of that communication?
The resulting analysis was far richer and far more relevant than anything my colleague and I (seasoned researchers both) could have arrived at alone. The participatory data analysis process allowed for stakeholders to provide both context and insights into the evaluation findings. The most meaningful findings for the program stakeholders were identified. Recommendations were rooted in the collective wisdom of program providers, managers and patients and were informed by a practical and intimate understanding of the environment in which the program is situated. Stakeholders got to tell us – the evaluators – how most effectively to report the findings and recommendations, increasing the possible impact of our report and the likelihood that the recommendations will be acted upon.
In addition, the meaning making session – two and a half hours carved out of the personal time of some very busy professionals and patients – gave a diverse group of people, all connected by their interest in one healthcare program, an opportunity both to develop and to strengthen relationships, to mark and to celebrate successes, to mull over and identify possible solutions to challenges, and ultimately to confirm their place as fellows in the same community.
My colleague and I listened carefully to the discussion, we gently prompted and probed, and we faithfully recorded all of the ideas, reflections, insights and recommendations. We left the session feeling privileged to have witnessed the commitment, energy, intelligence and compassion of the participants and feeling very satisfied with the quality and depth of analysis that the process had generated.
We’re looking forward to an opportunity to host our next Data Party as well as exploring other participatory data analysis tools and activities.
(For an excellent, hands-on guide to participatory data analysis methods, check out Dabbling in the Data by Public Profit: http://www.publicprofit.net/Dabbling-In-The-Data)