In emergency response, the quality of our data and reporting plays a critical role in how we plan, allocate resources, and respond to people in urgent need. High-quality data helps ensure our actions are informed, timely, and equitable. However, when data collection methodologies are weak or inconsistent, the consequences can be significant: - Data may not be valid, leading to unreliable conclusions. - Assessments may be inaccurate or unnecessarily repeated. - Accessible areas are often oversampled, while harder-to-reach communities are left out. - Vulnerable groups risk being excluded. - Programs may rely on assumptions rather than actual evidence. To support field teams and strengthen MEARL practices in these complex environments, I developed a contextualized resource that outlines key challenges and practical mitigation strategies for rapid needs assessments in conflict-affected emergencies. While grounded in the Myanmar context, I believe the insights are relevant across many hum...
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