Data Quality Assurance
ASER applies a zero-tolerance policy toward data fraud, fabrication, manipulation, or “data cooking.” Any data that fails the required quality assurance checks is excluded from the final dataset, and fresh data are collected to replace the rejected records. ASER follows strict Standard Operating Procedures (SOPs) and operates a multi-layered Quality Assurance (QA) system, with clear separation between project implementation staff and independent QA personnel under the leadership of the Quality Assurance Manager.
The ASER QA system is built around the following key measures:
- Early and continuous communication with clients to ensure a clear understanding of project objectives and enable timely decisions and corrective actions.
- Recruitment of competent and ethical field staff with appropriate professional experience and integrity.
- Real-time data quality monitoring and spot checks throughout the field data collection process.
- Comprehensive training of field teams on project objectives, data collection procedures, ethical standards, and research protocols.
- Use of appropriate digital technologies to strengthen data collection efficiency, accuracy, and reliability, while allowing methodological flexibility where security or operational constraints require it.
- Development and contextualization of data collection tools to ensure they are suitable for the local setting and target population.
- Piloting of all data collection instruments before full-scale implementation to identify and address potential weaknesses.
- Independent QA monitoring throughout fieldwork, including surprise field visits, spot checks, and remote monitoring from ASER offices.
- Systematic review of translations to ensure linguistic accuracy, consistency, and contextual appropriateness.
- Rigorous verification of quantitative data and analysis, including cross-checking outputs to ensure statistical accuracy and analytical integrity.
- Triangulation of findings using information from multiple credible and independent sources to strengthen the validity of conclusions.
- Peer review of all reports and deliverables to ensure accuracy, consistency, methodological quality, and professional standards before submission to the client.