Data quality is fundamental for ensuring the validity and reliability of data-driven decision-making in the fields of Vulnerability Analysis and Mapping (VAM) and Monitoring functions. This guidance provides comprehensive resources to support quality assurance across all phases of data collection and management, ensuring that your food security and essential needs assessments and monitoring exercises are based on reliable data.
This page includes resources to help you plan, implement, and assess data quality throughout your data collection process:
The WFP Data Quality Practical Guidance Note: This organizational tool outlines best practices for ensuring the quality of quantitative data. It is structured to assist in every stage of the data lifecycle, from planning and data collection to management and analysis.
Checklist for Data Quality Assurance: A detailed checklist covering the critical steps to be taken before, during, and after data collection to maintain high data quality standards. This checklist is a quick reference to ensure no critical step is missed.
Questionnaire Design and Programming Guidance: Guidance on the design and programming of data collection tools, including examples of relevant constraints, warnings, and best practices for translations and testing.
Enumerator Training and Management Tools: Resources and tools for training enumerators, managing field teams, and ensuring consistent and accurate data collection.
High Frequency Checks (HFCs): Guidelines for conducting high-frequency checks during data collection to promptly identify and address data quality issues.
Data Cleaning and Management Procedures: Comprehensive guidance on data cleaning, including general cleaning steps, cleaning by module, and best practices for data and document management.
Issue Log and Recommended Actions: A template for logging data quality issues and a guide for recommended actions to address major data quality concerns.
Available resources:
Data Quality Guidance Note: An in-depth document detailing best practices for ensuring data quality throughout the data collection process.
Data Quality Guidance Checklist: An short checklist to provide an overview of which data quality measures are already taken and where improvements could be made.
Scripts in Python, R, STATA and SPSS and sample data are available on GitHub for cleaning the expenditures module.
For more information, please contact the Needs Assessments and Targeting Unit in HQ VAM at global.assessmentandtargeting@wfp.org.