Data analysis is a transformative step in program evaluation, turning raw information into actionable insights that guide organizational decision-making. This article explores qualitative, quantitative, and mixed-methods approaches, emphasizing their complementary roles in nonprofit evaluation. Thematic, content, and narrative analysis provide depth into participant perspectives, while descriptive and inferential statistics generate measurable evidence of impact. Mixed-methods integration through triangulation and data correlation strengthens validity and produces holistic assessments. Case illustrations from literacy, vocational training, health, and microfinance initiatives demonstrate how nonprofits can translate findings into credible, evidence-based conclusions. Challenges such as contextual interpretation, benchmarking, and external influences are addressed, with emphasis on systematic documentation, reliability, and transparency. By adopting structured and integrated analysis strategies, organizations can optimize accountability, strengthen sustainability, and enhance their contributions to long-term development outcomes.
Anna Neya Kazanskaia (Wed,) studied this question.