Abstract Information: A major purpose of many program evaluations is to generate data for decision making. However, how can we be sure that our data are of good enough quality to make well informed decisions? While evaluators may receive training in aspects of data quality, overarching ways to enhance and manage data quality are rarely addressed. In this workshop, evaluators will be introduced to a comprehensive data quality management system for quantitative data, first developed by the Global Fund and several international development agencies, that consists of specific data quality assessment criteria and standard operating procedures. Through large and small group discussions, participants will first identify their own data quality issues. Participants will then review and relate their own experiences to certain assessment criteria and identify procedures for strengthening the quality of their data. Lastly, participants will review the basic components of a Data Quality Management Plan.
Relevance Statement: Data quality refers to the worth and accuracy of information collected and focuses on ensuring that the processes of data capturing, verification, and analysis are of a high standard. Data quality is about being committed to the accuracy of data for the purposes of accountability and utility. If the purposes of an evaluation effort are to use data for decision-making, to accurately report to stakeholders, or to improve programming and learning - then ensuring data is of high quality is critical. With the importance of data quality in mind, this workshop will first introduce participants to data quality concepts and assessment criteria, then examine data quality standard operating procedures and finally, review the components of a data quality management plan. In doing so, participants will identify their own data quality issues, develop appropriate standard operating procedures, and be able to complete their own data quality management plans.
