Data Collection

Data collection is a critical phase in food security and essential needs assessments, providing the foundation for robust analysis and evidence-based decision-making. High-quality data collection depends on careful preparation, well-trained enumerators, and rigorous quality assurance at every step.

Key Steps in Data Collection

• Questionnaire Design: Develop a clear, relevant, and well-structured questionnaire that translates assessment objectives into measurable questions. Ensure the tool is pre-tested, translated, and adapted to the local context

• Survey Designer: Use digital platforms such as Survey Designer to program questionnaires, manage skip logic, and streamline data collection workflows. Digital tools help reduce errors and enable real-time monitoring

• Enumerator Training and Testing: Train enumerators on the assessment objectives, questionnaire content, data collection protocols, and ethical considerations. Conduct a test to ensure enumerators are fully prepared for fieldwork

• Remote Surveys: When in-person data collection is not feasible, remote mVAM surveys can be used to reach respondents. These methods require adapted questionnaires, specific training, and additional quality controls to ensure data reliability

• Field Protocols and Supervision: Establish clear field protocols for logistics, safety, and communication. Provide ongoing supervision and support to enumerators to maintain data quality and address challenges in real time

• Data Quality Assurance: Apply rigorous quality assurance measures before, during, and after data collection. Use checklists, high-frequency checks, and data cleaning protocols to minimize errors and ensure reliability