In NYC P-TECH Grades 9-14 schools, students take an integrated sequence of high school and college courses with the goal of completing both high school and college, while simultaneously being exposed to hands-on work experiences. This infographic describes the model and introduces MDRC’s evaluation of it.
The Center for Applied Behavioral Science (CABS) combines MDRC’s decades of experience tackling social policy issues with insights from behavioral science. This graphic explains the CABS’s approach to solving problems.
The SIMPLER framework was developed for the Behavioral Interventions to Advance Self-Sufficiency (BIAS) project ― the first major effort to apply behavioral insights to human services programs in the United States. SIMPLER summarizes several key behavioral concepts that can guide practitioners interested in using behavioral insights to enhance service delivery.
As the first major effort to use a behavioral economics lens to examine human services programs that serve poor and vulnerable families in the United States, the BIAS project demonstrated the value of applying behavioral insights to improve the efficacy of human services programs.
A Primer for Researchers Working with Education Data
Predictive modeling estimates individuals’ probabilities of future outcomes by building and testing a model using data on similar individuals whose outcomes are already known. The method offers benefits for continuous improvement efforts and efficient allocation of resources. This paper explains MDRC’s framework for using predictive modeling in education.
Using an alternative to classical statistics, this paper reanalyzes results from three published studies of interventions to increase employment and reduce welfare dependency. The analysis formally incorporates prior beliefs about the interventions, characterizing the results in terms of the distribution of possible effects, and generally confirms the earlier published findings.
An Empirical Assessment Based on Four Recent Evaluations
This reference report, prepared for the National Center for Education Evaluation and Regional Assistance of the Institute of Education Sciences (IES), uses data from four recent IES-funded experimental design studies that measured student achievement using both state tests and a study-administered test.
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.
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.