Data Quality Considerations

1. Data Quality Considerations M&E Capacity Strengthening Workshop, Maputo 19 and 20 September 2011 Arif Rashid, TOPS

2. Data Quality Project Implementation Project activities are implemented in the field. These activities are designed to produce results that are quantifiable. Data Management System An information system represents these activities by collecting the results that were produced and mapping them to a recording system. Data Quality: Howwell the DMS represents the fact ? True picture of the field Data Management System Slide # 1

3. Why Data Quality? • Program is “evidence-based” • Data quality  Data use • Accountability Slide # 2

4. Conceptual Framework of Data Quality? Quality Data Data management and reporting system M&E Unit in the Country Office Intermediate aggregation levels (e.g. districts/ regions, etc.) Service delivery points Slide # 3

5. Dimensions of data quality • Accuracy/Validity • Accurate data are considered correct. Accurate data minimize error (e.g., recording or interviewer bias, transcription error, sampling error) to a point of being negligible. • Reliability • Data generated by a project’s information system are based on protocols and procedures. The data are objectively verifiable. The data are reliable because they are measured and collected consistently. Slide # 4

6. Dimensions of data quality • Precision • The data have sufficient detail information. For example, an indicator requires the number of individuals who received training on integrated pest management by sex. An information system lacks precision if it is not designed to record the sex of the individual who received training. • Completeness • Completeness means that an information system from which the results are derived is appropriately inclusive: it represents the complete list of eligible persons or units and not just a fraction of the list. Slide # 5

7. Dimensions of data quality • Timeliness • Data are timely when they are up-to-date (current), and when the information is available on time. • Integrity • Data have integrity when the system used to generate them are protected from deliberate bias or manipulation for political or personal reasons. Slide # 6

8. Dimensions of data quality • Confidentiality • Confidentiality means that the respondents are assured that their data will be maintained according to national and/or international standards for data. This means that personal data are not disclosed inappropriately, and that data in hard copy and electronic form are treated with appropriate levels of security (e.g. kept in locked cabinets and in password protected files. Slide # 7

9. Data quality Assessments Project participants Managers Technicians Field staff Local Govt. Partners Headquarters Slide # 8

10. Data quality Assessments • Two dimensions of assessments: • Assessment of data management and reporting systems • Follow-up verification of reported data for key indicators (spot checks of actual figures) Slide # 9

11. Systems assessment tools Slide # 10

12. Systems assessment tools Slide # 11

13. Schematic of follow-up verification Slide # 12

14. M&E system design for data quality • Appropriate design of M&E system is necessary to comply with both aspects of DQA • Ensure that all dimensions of data quality are incorporated into M&E design • Ensure that all processes and data management operations are implemented and fully documented (ensure a comprehensive paper trail to facilitate follow-up verification) Slide # 13

15. This presentation was made possible by the generous support of the American people through the United States Agency for International Development (USAID). The contents are the responsibility of Save the Children and do not necessarily reflect the views of USAID or the United States Government.