By Andréanne Chu Breton-Carbonneau.
How can participatory action research with trusted community-based organizations ensure that communities most impacted take part in interpretating the data, turning findings into deeper insights and more meaningful community-led solutions?
Participatory content analysis is a final step in participatory action research and enables a community research team to analyze data to identify content themes, visually map relationships, and derive actionable insights based on local knowledge and lived expertise. The community research team comprises academic researchers, community-based organization partners, and “resident researchers,” who are community members recruited—with support from the community-based organization partners—from groups most impacted by the research area.
Five typical steps
Although the participatory content analysis process may vary depending on the research content and context, it typically involves two preparatory steps followed by three main steps.
1. Preliminary synthesis of findings
After the community research team has completed data collection, the academic researchers preprocess and familiarize themselves with the raw qualitative dataset to develop preliminary content categories (early coding schemes) to support the participatory content analysis process.
2. Introduction to data analysis
A community training session on data analysis introduces the community members to key concepts and various approaches to qualitative analysis, so that they can meaningfully engage in the participatory content analysis process.
3. Linking and interpreting content themes
In this first main step, participants review the preliminary content categories developed in preparatory step 1, along with excerpts from the pre-processed dataset. Participants independently interpret these materials, grouping the categories into broader themes, and identifying connections across the data. This individual work is then followed by small-group discussions, where participants explain the connections they observed.
4. Visually modeling relationships between content themes
While working in their small groups, participants connect the broader themes to build a conceptual map that visually represents how the themes relate and form a larger narrative. Each group then presents its model to the full group to co-create a shared version. Together, participants review and refine the collective model to ensure it is comprehensive and accurately reflects their experience of these relationships.
5. Making sense of the results
Participants reflect on how the model and identified themes can inform the development of actionable, community-led solutions. They begin by individually journaling about the significance of the findings and their potential relevance to addressing the problem at hand. Afterwards, participants share their reflections with the group and take part in a collective brainstorming session to identify priorities and next steps.
Skills and resources needed
Facilitators need strong skills in participatory methods, and experience in managing power imbalances among academic researchers, community-based organizations and resident researchers. They should also have demonstrated experience in building community trust and in practicing cultural humility. Effective facilitators should be prepared to improvise, adapting materials and activities in response to participants’ needs and priorities, even when these differ from the original research agenda.
Academic researchers serve as guides, contributing qualitative research knowledge and skills to support collective analysis, community exploration, and meaning making. Their role should bring methodological expertise into the process in a way that complements lived and community-based expertise.
Helpful resources can include:
- compensation for community member contributions to recognize their local expertise
- translation and interpretation services, childcare, transportation vouchers
- pre-developed templates and worksheets to present preliminary findings and help participants link and model content themes.
Finally, effective communication and plain language are critical and communal meals can foster connections.
Strengths and weaknesses
Most of the strengths and weaknesses relate to both participatory content analysis and participatory action research more broadly.
In terms of strengths, participatory content analysis:
- prioritizes the challenges perceived by those closest to the problem, ensuring findings are directly relevant to community needs
- empowers community members by involving them in the research process, leading to increased ownership and commitment
- is based on authentic collaboration, reflecting lived experiences and insights that are often excluded or superficially addressed by traditional approaches
- builds trust and reciprocity within the community research team, offering a more comprehensive and nuanced analysis than traditional methods.
In terms of weaknesses, participatory content analysis:
- can lead to conflict or disagreement between participants and academic researchers over power relations, data interpretation and directions for action, necessitating skilled facilitation and conflict resolution
- can be more time consuming, costly, and difficult to manage than traditional qualitative analysis methods
- requires additional funding and support for community-based organizations to facilitate the recruitment of resident researchers and to support community-building efforts
- is difficult to evaluate using traditional monitoring and evaluation approaches.
Closing questions
What has your experience been with participatory content analysis? Are there other processes that you would recommend? Have you identified other skills and resources that are needed? What strengths and weaknesses have you observed in these approaches?
To find out more:
Breton-Carbonneau, A. C. (2025). Participatory content analysis. Participatory research methods for sustainability – toolkit #12. GAIA, 34, 1: 51 – 54. (Online) (DOI): https://doi.org/10.14512/gaia.34.1.10
Much of the text of this i2Insights contribution is taken verbatim from this paper, which also includes the references.
To see all blog posts from the partnership with the journal GAIA: https://i2insights.org/tag/partner-gaia-journal/. Thanks to GAIA for making Andréanne Chu Breton-Carbonneau’s paper free to access until 2 March, 2026.
Use of Generative Artificial Intelligence (AI) Statement: Generative artificial intelligence was not used in the development of this i2Insights contribution. (For i2Insights policy on generative artificial intelligence please see https://i2insights.org/contributing-to-i2insights/guidelines-for-authors/#artificial-intelligence.)
Biography: Andréanne Chu Breton-Carbonneau PhD is a postdoctoral fellow at the Barcelona Laboratory for Urban Environmental Justice and Sustainability (BCNUEJ) at the Universitat Autònoma de Barcelona (UAB), Institute of Environmental Science and Technology (ICTA), in Barcelona, Spain. She studies how community-led research can advance climate adaptation, climate justice, and health equity by shaping participatory urban governance and planning.