Part 1: What Is Data Triangulation In Evaluation?
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A strong evaluation doesn’t rely on a single perspective. It builds credibility by weaving together multiple sources of evidence. That’s where data triangulation comes in.
Triangulation is the process of bringing together different sources, methods, and perspectives to strengthen the rigour of your analysis. It can also help you tell a more complete story, spot patterns across diverse voices, and demonstrate that your conclusions are rooted in more than one perspective. It’s a key step in moving from raw data to meaningful, actionable insights.
This article is the first in a two-part series on data triangulation in evaluation.
Part 1 explores what triangulation is and why it matters
Part 2 offers practical tips for how to present triangulated findings in your report
Whether you're new to evaluation or refining your reporting practices, this series will help you apply triangulation to create stronger, more credible reports.
What is data triangulation?
Data triangulation refers to the use of multiple data sources to explore the same evaluation question or issue. This method doesn’t just confirm what the data is telling you; it can also surface contradictions, add context, and strengthen your conclusions. There are several ways evaluators can triangulate data:
1. Different target group perspectives
Drawing on input from a variety of individuals who experience the program or intervention to gather diverse perspectives, for example:
Program participants who receive services
Staff who deliver the program
Partners or funders who support or oversee implementation
2. Different data collection methods
Using a mix of qualitative and quantitative methods to provide a more complete picture. For example:
Surveys might show trends or overall satisfaction
Interviews could reveal detailed personal experiences
Focus groups might uncover group dynamics or shared opinions
Observations may validate or challenge what people report
To learn more about mixed methods, check out our article “The Mixing in Mixed Methods”.
3. Different time points
Collecting data at multiple stages of a program helps evaluators understand how perceptions, outcomes, or implementation processes evolve over time. For example:
Pre-program data to establish a baseline
Mid-program check-ins to track progress and inform ongoing learning and real-time adjustments while the program is still being implemented
Post-program follow-up to assess outcomes and sustainability
Why use data triangulation?
Data triangulation strengthens your evaluation by bringing multiple perspectives to a single issue. The goal isn’t just to confirm the main themes and findings; it’s to construct a richer, more trustworthy understanding of your data.
When data from different sources align, it reinforces the credibility of your conclusions. When they differ, those discrepancies can prompt critical reflection:
Why might staff and participant perspectives diverge?
Does a drop in engagement align with a change in program delivery?
Are there emerging outcomes visible in one dataset that others have missed?
This layered approach is especially valuable in complex evaluations where multiple variables, audiences, and contexts interact. Triangulation allows evaluators to better navigate that complexity, offering a more complete and balanced interpretation of what’s happening and why.
Key benefits of data triangulation
✔ Enhances credibility and trustworthiness by confirming findings across multiple sources, reducing reliance on any one perspective or dataset
✔ Uncovers discrepancies and diverse viewpoints, prompting deeper reflection and more accurate interpretation of the data
✔ Provides a fuller, more nuanced picture by integrating qualitative and quantitative insights, as well as target group experiences
✔ Improves the quality of conclusions and recommendations by grounding them in evidence from different angles
✔ Demonstrates methodological rigour and ethical responsibility to key audiences by showing a thorough, balanced approach
✔ Supports transparency and accountability by clearly showing how data were collected, analyzed, and interpreted across sources
✔ Strengthens decision-making and learning by highlighting patterns, gaps, and areas for improvement that might be overlooked with a single-source approach
When developing your evaluation plan, it’s important to intentionally build in opportunities for data triangulation from the outset. This means identifying complementary data sources, methods, and perspectives that can be combined to validate findings and offer a richer understanding of the issue or program. Planning for triangulation early ensures that timelines, resources, and data collection tools are aligned to capture information from multiple angles.
In Part 2, we’ll walk through how to present triangulated findings clearly and responsibly, while also considering some of the challenges and limitations that can arise.