Rethinking Evaluation: The Case for Collaborative Data Analysis.
What happens when social service programs rely solely on internal teams to evaluate impact? They leave out critical insights from their community members. This ultimately leads to programs that are misaligned with actual needs, overlook key challenges, and fail to achieve their intended outcomes. It happens way too often.
Participatory Action Research (PAR) bridges this gap by centering community voices in the evaluation process. Unlike traditional evaluation methods, PAR fosters collaboration, equity, and trust by treating community members as co-creators rather than subjects of research. This article explores the transformative potential of PAR and dives into one of its most powerful practices: collaborative data analysis.
What is Participatory Action Research?
PAR is an inclusive and equitable approach to evaluation that prioritizes community involvement at every stage. It’s more than a research method - it’s a mindset shift that recognizes the value of lived experiences in shaping effective solutions. By fostering transparency and shared ownership, PAR helps ensure that programs holistically reflect the needs and priorities of the people they aim to serve.
How PAR Comes to Life:
PAR can be implemented through various methods that prioritize inclusion and shared ownership. Here's a few:
Intentional Engagement: Integrate community feedback into every stage of your programs, from planning to evaluation.
Co-Create Assessments: Partner with community members to design data collection tools that capture meaningful and context-specific insights.
Collaborative Data Analysis: Involve community voices in analyzing findings to ensure conclusions resonate with their lived experiences.
Collaborative Reporting: Share preliminary results and work together to develop insightful and actionable reports.
Among these methods, collaborative data analysis stands out as a powerful way to ensure that your insights truly align with the realities of the community you serve. Let’s explore how this practice works and why it’s essential.
What Does Collaborative Data Analysis Look Like?
Collaborative data analysis invites community members to engage deeply with research findings. It creates space for diverse voices to shape program narratives and guide decisions.
Why It’s Essential:
Equitable Decisions: Community insights ensure actions are informed by real, lived experiences, making solutions more effective and fair.
Better Alignment: Shape programs to meet actual needs, not assumptions.
Enhanced Trust: Collaborative processes build stronger, more transparent relationships between organizations and communities.
What Happens Without It:
When organizations interpret data in isolation, they risk:
Misalignment: Programs may implement solutions that don’t address the root causes of community challenges.
Missed Insights: Critical issues may remain hidden without diverse perspectives.
Eroded Trust: Communities may feel unheard, weakening relationships and program outcomes.
A Practical Solution: Data Walks
Data walks, a practice developed by The Urban Institute, are an innovative approach to collaborate data analysis. These interactive sessions invite community stakeholders — such as residents, service providers, researchers, and policymakers - to engage with data about their community, discuss preliminary insights, generate meaning and co-develop solutions to improve the design and implementation of programs.
What a successful data walk involves:
Accessible Presentation: Data is shared in visual, easy-to-understand formats like posters or infographics.
Facilitated Discussion: A trained facilitator guides conversations to ensure inclusive participation.
Actionable Feedback: Discussions inform real-time adjustments to programs and services.
Case Study: Addressing Racial Disparities in Reentry Programs
KGS Consulting supported a government agency in evaluating their criminal justice reentry programs. Initial findings suggested large racial disparities in participant access and success rates. While these findings hinted at issues with program implementation, engaging the community in a data walk revealed deeper insights that allowed us to:
Gain a holistic perspective: Several participants shared that they don't live in a racially diverse area. Therefore, some of the disparities observed were a reflection of regional demographics, not programmatic flaws.
Identify external influences. We also learned that disparities related to program access were a result of a referral process outside of the program manager's visibility or control.
Co-develop solutions: Through these discussions we were able to effectively target programs with racial disparities, identify root causes, and develop localized solutions such as increased collaboration with referral sources.
This collaborative approach transformed the evaluation process, leading to impactful solutions tailored to the community’s unique context.
Take the First Step
Interested in bringing practices like collaborative data analysis to your program? KGS Consulting can guide you every step of the way. Book a consultation today to start transforming your program’s impact.
Learn more about data walks from The Urban Institute here.
