Improving the Quality of Data and Impact-Evaluation Studies in Developing Countries
Stecklov, Guy; Weinreb, Alex
While the science of program evaluation has come a tremendous distance in the past couple of decades, measurement error remains a serious concern and its implications are often poorly understood by both data collectors and data analysts. The primary aim here is to offer a type of "back-to-basics" approach to minimizing error in developing country settings, particularly in relation to impactevaluation studies. Overall, the report calls for a two-stage approach to dealing with mismeasurement. In the first stage, researchers should attempt to minimize mismeasurement during data collection, but also incorporate elements into the study that allow them to estimate its overall dimensions and effects on analysis with more confidence. Econometric fixes for mismeasurement¿whose purview is limited to a smaller subset of errors¿then serve as a secondary line of defense. Such a complementary strategy can help to ensure that decisions are made based on the most accurate empirical evaluations.