Improving the Quality of Data and Impact-Evaluation Studies in Developing Countries

Stecklov, Guy;
Weinreb, Alex
May 2010
While the science of program evaluation has come a tremendous distance in thepast 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.