Course Modules

Module 1: Introduction to Data Quality Concepts

• Definitions and importance of data quality
• Dimensions of data quality (e.g., accuracy, timeliness, coherence)
• Overview of the UN Fundamental Principles of Official Statistics
• The link between data quality and policy reliability
• Case Study: How poor-quality data affected drought response planning

Module 2: Quality Frameworks and Standards

• Global statistical quality frameworks (e.g., IMF DQAF, Eurostat)
• National statistical quality assurance frameworks (NSQAF)
• Role of NSOs in enforcing quality standards
• Benchmarking quality practices against international norms
• Case Study: Applying IMF DQAF in national account statistics

Module 3: Planning for Data Quality

• Designing quality assurance plans
• Setting performance indicators for data quality
• Allocating resources and responsibilities for quality
• Risk assessment and mitigation planning
• Case Study: Data quality planning in a multi-sector baseline survey

Module 4: Ensuring Quality in Data Collection

• Training enumerators and field protocols
• Designing quality control checklists
• Supervising field teams and real-time verification
• Using technology for quality tracking in the field
• Case Study: Enhancing survey quality with mobile validation tools

Module 5: Quality Assurance in Data Processing

• Identifying common data entry and coding errors
• Validating and cleaning datasets
• Tracking processing errors with audit trails
• Managing version control and documentation
• Case Study: Implementing automated data checks in a labor force survey

Module 6: Statistical Analysis and Output Validation

• Checking internal consistency of data
• Comparing findings with benchmarks or previous years
• Outlier detection and treatment
• Validating data summaries and interpretation
• Case Study: Quality checks in GDP estimation

Module 7: Quality Reporting and Dissemination

• Creating quality statements and metadata
• Transparency in reporting limitations and errors
• Documenting methods and changes in methodology
• Promoting user confidence in published data
• Case Study: Publishing quality reports alongside annual statistical abstracts

Module 8: Institutionalizing Quality Assurance Systems

• Embedding QA in organizational workflows
• Establishing QA teams or committees
• Creating SOPs for quality control
• Integrating QA with M&E systems
• Case Study: Establishing QA procedures in a Ministry of Planning

Module 9: Monitoring, Evaluation, and Continuous Improvement

• Conducting periodic quality reviews and audits
• Feedback loops from data users
• Updating QA strategies based on lessons learned
• Using KPIs to track QA effectiveness
• Case Study: Continuous improvement in population census operations

Module 10: Promoting a Culture of Quality in Statistics

• Leadership and staff buy-in for quality
• Training and sensitization of staff
• Rewarding and recognizing QA contributions
• Aligning QA with organizational performance goals
• Case Study: Building a culture of data quality in the public sector