In a speech given at a conference sponsored by the French government on the role of experimental studies in reducing poverty, MDRC President Gordon Berlin described how the results of random assignment studies have acted as powerful levers for changing social policy in the United States.
No universal guideline exists for judging the practical importance of a standardized effect size, a measure of the magnitude of an intervention’s effects. This working paper argues that effect sizes should be interpreted using empirical benchmarks — and presents three types in the context of education research.
In these remarks, delivered at Speaker Nancy Pelosi’s National Summit on America’s Children on May 22, MDRC President Gordon Berlin summarizes rigorous research evidence showing that supplementing the earnings of parents helps raise families out of poverty and improves the school performance of young children.
In his testimony before the House Ways and Means Subcommittee on Income Security and Family Support, MDRC President Gordon Berlin argues that the most direct way to alleviate poverty is to tackle the legacy of falling wages, particularly for men with less education.
This MDRC research methodology working paper examines the core analytic elements of randomized experiments for social research. Its goal is to provide a compact discussion of the design and analysis of randomized experiments for measuring the impact of social or educational interventions.
Presented Before the Subcommittee on Federalism and the Census, House Committee on Government Reform
MDRC’s study of Jobs-Plus, an employment program for public housing residents, offered the first hard evidence that a work-focused intervention based in public housing can effectively boost residents’ earnings and promote their self-sufficiency. Congress may wish to consider introducing Jobs-Plus in additional housing developments across the country.
Empirical Guidance for Studies That Randomize Schools to Measure the Impacts of Educational Interventions
This paper examines how controlling statistically for baseline covariates (especially pretests) improves the precision of studies that randomize schools to measure the impacts of educational interventions on student achievement.