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Collecting High-Quality Data

This article explores the significance of data quality in monitoring and evaluation plans. It discusses the elements of data quality, including validity, reliability, precision, completeness, timeliness, and integrity. It also provides helpful resources for developing data quality plans.

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Collecting High-Quality Data

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  1. Collecting High-Quality Data

  2. Part of the M&E Plan

  3. What is Data Quality? ActualResults Reported Results ? Data Quality: How well our M&E data “tell the true story.” Adapted from: http://www.cpc.unc.edu/measure presentation by Win Brown, USAID/South Africa, School of Health Systems and Public Health, Monitoring and evaluation of HIV/AIDS Programs, Data Quality; March 2, 2011.

  4. Elements of Data Quality Adapted from: http://www.cpc.unc.edu/measure presentation by Win Brown, USAID/South Africa, School of Health Systems and Public Health, Monitoring and evaluation of HIV/AIDS Programs, Data Quality; March 2, 2011.

  5. Validity and Reliability: Hitting the Target NOT Valid NOTReliable Reliable but NOT valid Reliable AND Valid!!! X X X X X X X X X X XXX XXXX XXX XXX XXXX XXX Adapted from: http://www.cpc.unc.edu/measure presentation by Win Brown, USAID/South Africa, School of Health Systems and Public Health, Monitoring and evaluation of HIV/AIDS Programs, Data Quality; March 2, 2011.

  6. Precision Which indicator description will yield the most precise result?

  7. Completeness • Often related to: • ease of collecting and reporting data • data sources • training

  8. Timeliness • Are we meeting internal and external deadlines? • Communicate expectations clearly. • Offer support to collect/analyze where needed (budget?). • Are we analyzing results often enough to be useful for program management? • The sooner we know about a problem, the sooner we can fix it!

  9. Integrity • Often difficult and sensitive topic. • Routine verification from the startcan help avoid bias of any kind. • A partner submits perfect reports every month on time and meets or exceeds targets. • A partner submits reports with a few errors every month, sometimes 1-2 days late; usually meets or comes close to targets. • Which data would you verify and why?

  10. Data Quality Plans

  11. Helpful ResourcesMEASURE Evaluation Project Data Quality http://www.cpc.unc.edu/measure/tools/monitoring-evaluation-systems/data-quality-assurance-tools Data Use http://www.cpc.unc.edu/measure/our-work/data-demand-and-use

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