MampE Capacity Strengthening Workshop Addis Ababa 4 to 8 June 2012 Arif Rashid TOPS Project Implementation Project activities are implemented in the field These activities are designed to produce results that are quantifiable ID: 265343
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Slide1
Dimensions of Data Quality
M&E Capacity
Strengthening Workshop,
Addis Ababa
4 to 8 June 2012
Arif Rashid, TOPSSlide2
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:
How
well the DMS represents the fact
True picture of the field
Data Management System
Data Quality
?
Slide # 1Slide3
Why Data Quality?
Program is “evidence-based”
Data quality
Data use
Accountability
Slide # 2Slide4
Conceptual Framework of Data Quality
Service delivery points
Intermediate aggregation levels
(e.g. districts/ regions, etc.)
M&E Unit in the Country Office
Data management and reporting system
Functional components of Data Management Systems Needed to Ensure Data Quality
M&E Structures, Roles and Responsibilities
Indicator definitions and reporting guidelines
Data collection and reporting forms/tools
Data management processes
Data quality mechanisms
M&E capacity and system feedback
Dimensions of Data Quality
Validity, Reliability, Timeliness, Precision, Integrity
Quality Data
Slide # 3Slide5
Dimensions of data quality
Validity
Valid or accurate data are considered correct. Valid 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.
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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.
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 # 5Slide7
Data Quality: Assurance and Assessment
Data Quality Assurance
- A process for defining the appropriate dimensions and criteria of data quality, and procedures to ensure that data quality criteria are met over timeData Quality Assessment –Review of project M&E system to ensure that quality of data captured by the M&E system is acceptable.
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What’s a Data Quality Assessment (DQA)?
Slide # 7
A data quality assessment is a periodic review that:
Helps Food for Peace and the implementing partner determine and document “How good are the data?”
Provides an opportunity for capacity-building of implementing partners.
DQAs are required of all USAID data that are reported to the federal government. It is a requirement by the US Government.Slide9
Data quality Assessments
Local Govt.
Managers
Technicians
Field staff
Partners
Headquarters
Project participants
Slide # 8Slide10
Components of DQA (1/2)
Assess four main dimensions of
data collection process:Design
Organizational structure Implementation practicesFollow-up verification of reported data
Slide # 9Slide11
Components of DQA (2/2)
Systems assessment
of data
m
anagement and reporting
Are systems and practices in place to collect, aggregate, analyze the appropriate information?
Are these systems and practices being followed?
Verification
of reported data for key indicators
Spot checks to find non-sampling errorsSlide # 10Slide12
M&E Systems Assessment Tools
M&E structures, functions and capabilities
1
Are key M&E and data-management staff identified with clearly assigned responsibilities?
2
Have the majority of key M&E and data management staff received the required training?
Indicator definitions and reporting guidelines
3
Are there operational indicator definitions meeting relevant standards that are systematically followed by all service points?
4
Has the project clearly documented what is reported to who, and how and when reporting is required?
Data collection and reporting forms/tools
5Are there standard data-collection and reporting forms that are systematically used?
6
Are data recorded with sufficient precision/detail to measure relevant indicators? 7
Are source documents kept and made available in accordance with a written policy?
Slide # 11Slide13
M&E Systems Assessment Tools
Data management
processes
8
Does clear documentation of collection, aggregation and manipulation steps exist?
9
Are data quality challenges identified and are mechanisms in place for addressing them?
10
Are there clearly defined and followed procedures to identify and reconcile discrepancies in reports?
11
Are there clearly defined and followed procedures to periodically verify source data?
M&E capacity and system feedback
12Do M&E staff have clear understanding about the roles and how data collection and analysis fits into the overall program quality?
13
Do M&E staff have clear understanding with the PMP, IPTT and M&E Plan?
14
Do M&E staff have required
skills in data collection,
aggregation, analysis, interpretation and reporting
?
15
Are
there clearly defined feedback mechanism to improve data and system quality?
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Schematic of follow-up verification
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Practical DQA Tips
Build assessment into normal work processes
Use software checks and edits of data on computer systems
Get feedback from users of the data
Compare the data with data from other sources
Obtain verification by independent parties
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DQA realities!
The general principle is that performance data should be as complete, accurate and consistent as management needs and resources permit. Consequently, DQAs are not intended to be overly burdensome or time intensive
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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 # 16Slide18
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.