Data analysis and interpretation are central to Monitoring and Evaluation (M&E), transforming raw data into actionable insights that guide program design, decision-making, and accountability. This article explores quantitative and qualitative approaches, examining methods such as descriptive and inferential statistics, coding, thematic analysis, and narrative inquiry. Triangulation is emphasized as a critical strategy to enhance validity by integrating diverse data sources, methods, and perspectives. The discussion also highlights interpretation practices, including contextualization, data disaggregation, and effective communication with stakeholders. Special attention is given to low-resource settings, where organizations must rely on affordable tools, streamlined indicators, and local capacity building to maintain rigor. By combining methodological precision, ethical responsibility, and contextual sensitivity, the article shows how data analysis and interpretation strengthen program learning, equity, and long-term impact in international development.
Anna Neya Kazanskaia (Wed,) studied this question.