The Role of Data Quality Assurance in Enhancing Program Evaluation: Evidence from Evaluation Data in Organizations in Benin

Evaluation practice faces persistent challenges in ensuring reliability and validity, elevating the importance of data quality and prompting the development of Data Quality Assurance (DQA) mechanisms. However, empirical evidence linking DQA to evaluation data quality remains limited, particularly in Benin. This study addresses this gap by examining the relationships between DQA and evaluation data quality. The study analyzes ninety-seven evaluation reports collected in Benin through snowball sampling. Two master’s-level raters independently assessed the reports using the improved STROBE and COREQ guidelines, supplemented by gathered data metrics. Improved STROBE and COREQ promote transparent reporting, helping assess study quality. Inter-rater reliability exceeded 60% across all variables. Fixed effects modeling was conducted. The use of fixed effects in this causal framework enables robust comparisons, even in non-experimental sampling. Results, which satisfy all required assumptions, indicate that organizations implementing DQA exhibit significantly higher evaluation data quality than those without such mechanisms. Although the non-random sampling approach limits generalizability, the findings are not sensitive to sampling bias.