Part 2: How Do You Present Triangulated Data?
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This is part 2 of our series on data triangulation in evaluation.
In part 1, we looked at what triangulation is and why it matters in evaluation. In this article, we focus on how to clearly and effectively present triangulated findings in your evaluation report.
When writing a report, it’s not enough to simply share results. You also need to build credibility and show why your conclusions can be trusted. Triangulated data helps you do just that by drawing from multiple sources, methods, and perspectives.
How to Use Data Triangulation in an Evaluation Report
Here’s how you can apply triangulation when writing your evaluation report:
1. Use a triangulation matrix
A triangulation matrix is a practical tool that helps evaluators assess how different data sources contribute to answering key evaluation questions. Rather than presenting findings in isolation, this visual format brings together evidence from multiple methods, making it easier to compare, contrast, and synthesize insights. For example:
This format allows you to:
Quickly identify where findings are well-supported across methods
Spot inconsistencies or gaps that need further investigation
Organize large volumes of data into digestible insights
Increase transparency and trust in how conclusions were drawn
Note: While the triangulation matrix is a valuable internal tool for synthesizing and comparing data, it is not intended to replace the narrative findings section of your report. Instead, use it behind the scenes to help you identify key messages, areas of convergence, and points that need further explanation. The matrix can support your writing by ensuring your conclusions are evidence-informed and balanced, while the final report should weave those insights into a comprehensive and reader-friendly narrative.
2. Present multiple perspectives together
When summarizing findings, aim to integrate evidence from two or more data sources within the same section or paragraph. This helps illustrate how different forms of evidence converge or diverge to support your conclusions. For example:
“Survey responses indicated that 82% of staff felt confident using the new system. This was echoed in interviews, where staff described improved ease and speed in completing tasks. Observations during training sessions also showed staff engaging with fewer technical support requests, suggesting increased comfort with the system.”
It’s especially effective to align quantitative trends with qualitative insights. For example, backing up a high satisfaction rate with quotes or themes that illustrate why participants felt that way. This layered approach makes your findings more credible, relatable, and actionable.
3. Explain divergent findings
When data sources yield different or even conflicting findings, don’t overlook or minimize them. Explore them. Divergence is not necessarily a problem; it’s often a valuable clue that points to differences in experience, perception, or implementation. Contradictory data can deepen understanding and reveal important dynamics that would otherwise be missed. For example:
“While 82% of survey respondents rated the initiative positively, focus group participants, particularly those from rural areas, expressed concerns about accessibility and relevance. These differences may reflect variations in infrastructure, service delivery, or lived experience that the survey did not fully capture.”
Rather than undermining your report, acknowledging and analyzing divergent findings demonstrates critical thinking and evaluative rigour. It also signals transparency and helps avoid oversimplified conclusions.
When addressing divergence:
Identify who holds different perspectives (e.g., by role, geography, or demographic group)
Propose potential reasons for the discrepancy (e.g., differences in context, timing, or data collection method)
Consider the implications for your recommendations: do they need to be adapted for specific groups?
Be cautious about overgeneralizing from any single data source, especially if others suggest an alternative view
By discussing divergent findings, you build a more nuanced narrative, one that respects complexity and better informs decision-making.
4. Link triangulated findings to recommendations
Triangulated findings are a powerful foundation for building credible, relevant, and actionable recommendations. When multiple data sources point to the same insight, it strengthens the validity of your conclusions and demonstrates that your recommendations are not based on isolated opinions or single-method results. For example:
“Given consistent feedback from both participants and staff across surveys, interviews, and focus groups that post-program support was insufficient, we recommend integrating a structured follow-up component into the service delivery model.”
This kind of evidence-backed recommendation does the following:
Shows transparency in how conclusions were reached
Demonstrates methodological rigour to funders and decision-makers
Increases confidence that recommendations are reflective of diverse perspectives
Improves uptake by clearly linking actions to consistent findings
To maximize impact:
Reference which data sources support the recommendation (e.g., "both qualitative and quantitative feedback")
Highlight consistency across stakeholder groups when applicable
If findings are mixed, explain how the recommendation still responds to key concerns
Prioritize recommendations that address themes echoed across multiple methods and voices
Triangulation doesn’t just inform better evaluations; it helps drive better decisions. When your report clearly connects multiple lines of evidence to each recommendation, you make it easier for decision makers to act with confidence.
Limitations of Data Triangulation
While data triangulation strengthens evaluation quality, it’s not without trade-offs. Using multiple data sources or methods typically requires more time, effort, and resources from planning and data collection, to analysis and interpretation.
The analysis phase, in particular, can become more complex. Evaluators need to take care that in seeking patterns or consensus across data, they don’t overlook outliers, minority perspectives, or divergent findings. These differences can hold valuable insights, especially in complex or equity-focused evaluations.
Triangulation should enhance the richness of your findings, not flatten them.
Final Thoughts
Data triangulation isn’t just a research buzzword, it’s a practical, evidence-based approach that enhances the quality and credibility of your evaluation reporting. By intentionally comparing and integrating findings from multiple sources, you go beyond simply presenting data: you demonstrate that your conclusions are grounded, nuanced, and trustworthy.
As you write your next evaluation report, ask yourself:
Do the different data sources support or challenge each other?
Have I clearly shown how various perspectives shaped the findings?
Am I using triangulated evidence to justify my conclusions and recommendations?
When done well, triangulation doesn’t just make your evaluation more robust, it makes it more useful. It shows key audiences not just what you found, but why they can trust and act on it.