A Comprehensive Library of Community-Engaged Research Resources - Analyze Stage

Guidance for the Analyze Stage of CEnR

The analyze stage transforms collected data into shared understanding. At this stage, research teams review and refine their analysis plan, carry out data analysis, and interpret findings in partnership with the community. Community engagement in interpretation is essential across all CEnR approaches and helps ensure that results reflect real experiences and priorities. This stage involves the following:

• Revisiting and expanding the analysis plan: Teams clarify roles, responsibilities, and timelines for analysis and interpretation, building on what was developed during the plan stage.
• Building capacity for analysis and interpretation: Community partners receive training or support, as needed, to engage meaningfully in reviewing data and shaping findings.
• Conducting data analysis: Researchers and, in some cases, community partners analyze data using the agreed-upon methods.
• Interpreting findings: Early findings are shared with partners to invite discussion, surface new insights, and ensure that findings are relevant and accurate.

By approaching analysis with transparency and a commitment to reflecting on one’s own assumptions, this stage lays the foundation for results that are credible, useful, and centered on community priorities.

Revisit and Expand the Analysis Plan

Start by looking back at the agreements you made during the plan stage. Many CEnR teams outline roles and responsibilities early but, now that data have been collected, it’s time to revisit and expand those agreements.

Your analysis plan should clearly answer:

• Who is responsible for cleaning and organizing the data?
• Who will conduct initial coding or statistical analysis?
• Who will interpret the findings?
• How will input from community partners be gathered and incorporated?
• What process will be used to resolve disagreements about meaning or recommendations?
• What is your timeline and does it allow time for meaningful engagement?

In collaborative and empowered CEnR projects, the community may take part in reviewing data and identifying patterns. In advisory or consulted projects, researchers often conduct the technical analysis and invite the community into interpretation and reflection.

Build Capacity for Analysis and Interpretation

To help community partners contribute meaningfully to data analysis and interpretation when these tasks align with their role in the project, it is important to offer training that reflects the types of data being used and the methods being applied. The goal is not to turn everyone into researchers, but to create an environment in which all partners can engage with the data, ask questions, and share their insights with confidence.

For qualitative data, consider:

• Introducing basic coding (e.g., What is a theme?)
• Practicing with sample quotes or transcripts
• Using visual tools like color-coded sticky notes or shared docs

For quantitative data, you might:

• Explain key terms (e.g., percentages, averages)
• Walk through tables or charts in plain language
• Use simple graphs or templates to visualize data

Across all types of data, offer:

• Short prep sessions or handouts
• Opportunities for questions and discussion
• Clear reinforcement that experience is a form of expertise

These supports help build shared understanding and make analysis and interpretation more inclusive.

Analyze the Data

If community partners are involved in analyzing data, they can contribute in a range of ways depending on the type of data. In collaborative and empowered CEnR projects, community members may co-lead analysis activities. In consulted or advisory projects, they may offer input at key moments.

If analyzing qualitative data, community partners might:

• Review transcripts or quotes to identify key ideas or themes
• Participate in group coding sessions or workshops
• Help define or refine codes based on community language and values
• Point out patterns, gaps, or stories that might be missed by researchers

If analyzing quantitative data, community partners might:

• Look at tables, graphs, or charts to help interpret what the numbers mean
• Ask clarifying questions about trends or comparisons
• Share insights on how the data connect to community conditions
• Help spot unexpected results that need more exploration

Partners do not need to analyze all data or use technical tools to play an important role. Even reviewing a summary and naming what stands out can shape how findings are understood and used.

If your CEnR project does not include community partners in data analysis, consider how you might bring them into the interpretation process instead.

Interpret the Findings

Interpretation is where findings are placed in context and turned into shared meaning. Regardless of who conducted the analysis, community partner involvement in interpretation is essential for all CEnR projects. Even when community partners are not engaged in data cleaning or coding, their perspectives are critical for ensuring that findings reflect community experiences and priorities.

Share emerging insights early and often. Don’t wait until the end of the analysis process to engage partners. Sharing early findings in accessible formats (e.g., summaries, visualizations, short presentations) gives partners time to reflect, respond, and shape how the story of the data is told.

Interpretation strategies may include the following:

• Data walks, where themes or charts are displayed and reviewed in small groups
• Community reflection sessions, where partners discuss what findings mean and what stands out
• Storytelling, mapping, or visual activities to connect data with real-world experiences
• Review of quotes or case examples, especially in qualitative research, to check whether themes resonate

These activities help validate findings, identify gaps, and generate new insights. Community members may offer alternative explanations, raise questions, or point out patterns that researchers might miss. Interpretation is also a time to begin developing recommendations that are useful and actionable. When done well, this process builds trust, deepens understanding, and strengthens the relevance of the research.

Wrap Up

The analyze stage turns data into meaning, ensuring that findings reflect both the community’s experiences and the goals of the research. Community partners’ involvement in interpretation is essential across all CEnR projects, even if their role in technical analysis varies. Key activities during analyze include the following:

• Revisit and finalize the analysis plan to clarify roles, responsibilities, and decision making.
• Offer training on data analysis and interpretation, based on partner roles.
• Conduct data analysis using agreed-upon methods, with appropriate levels of community engagement.
• Share emerging findings early to invite partner feedback and validate key themes.
• Facilitate interpretation sessions to explore meaning, raise questions, and identify implications.
• Refine findings and develop recommendations rooted in both data and community experience.

By prioritizing shared interpretation, transparent communication, and thoughtful engagement, research teams build trust and produce findings that are credible, useful, and ready to inform action.

Analyze Resources

These resources provide practical tools and insights to support your work during the implement stage of your CEnR project. Resources are organized by project, with each project labeled according to where it generally falls on the CEnR spectrum. This structure helps you quickly identify relevant examples and guidance that align with your approach.