1. Collecting High-Quality Data

2. M&E Plan FrameworkIndicators Data Collection Data Quality Data Use and Reporting Evaluation Strategy Budget Part of the M&E Plan

3. Data Quality: How well our M&E data “tell the true story.” Data Quality: How well our M&E data “tell the true story.” Actual Results Reported Results Reported Results What is 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.http://www.cpc.unc.edu/measure

4. Elements of Data Quality Validity Data measure what they are supposed to measure. Reliability Everyone defines, measures, and collects data the same way—all the time. Completeness Data include all of the values needed to calculate indicators. No variables are missing. Precision Data have sufficient detail. Units of measurement are very clear. Timeliness Data are up to date. Information is available on time. Integrity Data are true. The values are safe from deliberate bias and have not been changed for political or personal reasons. 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.http://www.cpc.unc.edu/measure

5. Validity and Reliability: Hitting the Target NOT Valid NOT Reliable Reliable but NOT valid X X X X X X X X X X XXX XXXX XXX Reliable AND Valid!!! 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.http://www.cpc.unc.edu/measure

6. Precision IndicatorDescription Treatment success rateCure PLUS completed treatment IndicatorDescription Treatment success rate All patients in the cohort: - with smear conversion and - who completed full course negative smear at 5 months of treatment but do not PLUS meet cure definition DIVIDED BY: Total number of smear-positive patients in the treatment cohort MULTIPLIED BY: 100 Which indicator description will yield the most precise result?

7. Completeness Often related to: ease of collecting and reporting data data sources training NGO partner Number of members participating in social mobilization Comment Friends of TBNot available Participant log not maintained TB Matters10 TB HelplineNot available Unclear how to determine who actively participated Stop TB NOW!12 TOTAL ???

8. Timeliness 1.Are we meeting internal and external deadlines? Communicate expectations clearly. Offer support to collect/analyze where needed (budget?). 2.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 start can 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 How can we ensure: StrategyResources Validity Reliability Completeness Precision Timeliness Integrity

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