An earlier post in this series discussed considerations for reporting and interpreting cross-site impact variation and for designing studies to investigate such cross-site variation. This post discusses how those ideas were applied to address two broad questions in the Mother and Infant Home Visiting Program Evaluation.
Part I of this two-part post discussed MDRC’s work with practitioners to construct valid and reliable measures of implementation fidelity to an early childhood curriculum. Part II examines how those data can reveal associations between levels of fidelity and gains in children’s academic skills.
Lessons from the Grameen America Evaluation
In any study, there is a tension between research and program needs. This program’s group-based microloan model presented particular challenges for random assignment. Reflections in Methodology looks at how the research design was adapted to allow a fair test of the program’s effectiveness without hampering its ability to operate.
As an alternative to random assignment, a regression discontinuity design takes advantage of situations where program eligibility is determined by whether a score exceeds a threshold. With careful attention to assumptions, analysis, and interpretation, this quasi-experimental design can provide rigorous estimates of program effects. Reflections on Methodology outlines some considerations.
Schools use individual screening tests to identify students at risk of falling behind in their reading levels. Could predictive analytics, incorporating multiple composite and subsection scores from a series of tests over time, do a better job of identifying at-risk students? Reflections on Methodology gives an example of this approach.
Lessons from the Grameen America Formative Evaluation
Random assignment is prized for its rigor, but it’s not always feasible to carry out. This Reflections in Methodology post outlines other strong options for studying the effects of a program and illustrates the application of some key considerations in a specific context.
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.
A Guide for Researchers
Conducting multiple statistical hypothesis tests can lead to spurious findings of effects. Multiple testing procedures (MTPs) counteract this problem but can substantially change statistical power. This paper presents methods for estimating multiple definitions of power and presents empirical findings on how power is affected by the use of MTPs.
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.
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.