Analyzing Evaluation Data in Non-Profit Programs: Methods, Integration, and Practical Applications
Author: Dr. Anna Neya Kazanskaia
Publisher: NEYA Global Publishing
Article | NEYA Global Journal of Non-Profit Studies
Year: 2025
ORCID: https://orcid.org/0009-0009-5669-1676
DOI: https://doi.org/10.64357/neya-gjnps-pr-ev-bt-pr-07
Download Full Text PDF
This material forms part of the NEYA Global knowledge architecture. Institutional, organizational, professional, training, consulting, curriculum, program design, or implementation use requires an active license from NEYA Global
Explore Architecture →
Module Overview →
Request Institutional Access →
About the Article
Data analysis is the stage of program evaluation where evidence becomes insight. This article explores qualitative, quantitative, and mixed-methods approaches, showing how each contributes to robust and actionable findings. Thematic and content analysis highlight participant experiences and contextual influences, while descriptive and inferential statistics provide rigor and generalizability. Mixed-methods strategies are emphasized for their ability to triangulate results, enhance validity, and broaden scope. The article also discusses the interpretation of findings, the role of theoretical frameworks, and the importance of contextualization. Data visualization—from spreadsheets to interactive dashboards and GIS tools—is presented as essential for communicating results to diverse stakeholders. Finally, simplified approaches for low-resource environments demonstrate how accessibility and rigor can be combined. By aligning methodological sophistication with practical feasibility, data analysis ensures that non-profit evaluation generates knowledge that is both credible and transformative.
Key Topics
- Qualitative and quantitative data analysis
- Mixed-methods approaches and triangulation
- Interpretation and contextualization of findings
- Data visualization tools and dashboards
- Simplified approaches for low-resource contexts
- Strengthening accountability and decision-making
Suggested Citation
Kazanskaia, A. N. (2025). Analyzing Evaluation Data in Non-Profit Programs: Methods, Integration, and Practical Applications. NEYA Global Publishing. https://doi.org/10.64357/neya-gjnps-pr-ev-bt-pr-07
References
| Bange, C. (2017). Data visualization for business intelligence. BARC Research. | ||||
| Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa | ||||
| Chambers, R. (1994). Participatory rural appraisal (PRA): Challenges, potentials and paradigm. World Development, 22(10), 1437-1454. https://doi.org/10.1016/0305-750X(94)90030-2 | ||||
| Creswell, J. W., & Plano Clark, V. L. (2011). Designing and conducting mixed methods research (2nd ed.). Sage. | ||||
| Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage. | ||||
| Kazanskaia, A. N. (2025). Program Evaluation: Frameworks and Best Practices. NEYA Global Publishing. | ||||
| Kazanskaia, A. N. (2025). Data Analysis: Turning Information into Insight. NEYA Global Publishing. https://doi.org/10.64357/data-analysis-insight-2025 | ||||
| Kazanskaia, A. N. (2025). Advanced Data Analytics Techniques for Social Impact. NEYA Global Publishing. https://doi.org/10.64357/advanced-data-analytics-social-impact-2025 | ||||
| Kazanskaia, A. N. (2025). Tools for Strategic Evaluation. NEYA Global Publishing. https://doi.org/10.64357/tools-evaluation-2025 | ||||
| Kazanskaia, A. N. (2025). Analyzing Evaluation Data in Non-Profit Programs: Methods, Integration, and Practical Applications. NEYA Global Journal of Non-Profit Studies. https://doi.org/10.64357/neya-gjnps-pr-ev-bt-pr-07 | ||||
| Kazanskaia, A. N. (2025). Using Evaluation Results for Program Improvement in Non-Profit Organizations. NEYA Global Journal of Non-Profit Studies. https://doi.org/10.64357/neya-gjnps-pr-evl-fr-bst-pr-08 | ||||
| Patton, M. Q. (2008). Utilization-focused evaluation (4th ed.). Sage. | ||||
| Saldana, J. (2016). The coding manual for qualitative researchers (3rd ed.). Sage. | ||||
