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Lessons from a Simulation Study
This paper makes valuable contributions to the literature on multiple-rating regression discontinuity designs (MRRDDs). It makes concrete recommendations for choosing among existing MRRDD estimation methods, for implementing any chosen method using local linear regression, and for providing accurate statistical inferences.
Design Options for an Evaluation of Head Start Coaching
Using a study of coaching in Head Start as an example, this report reviews potential experimental design options that get inside the “black box” of social interventions by estimating the effects of individual components. It concludes that factorial designs are usually most appropriate.
Design Report
This report provides recommendations for an evaluation of coaching that may impact teacher and classroom practices in Head Start and other early childhood settings — including about the research questions; the design of the impact study, implementation research, and cost analysis; and logistical challenges for carrying out the design.
In many evaluations, individuals are randomly assigned to experimental arms and then grouped to receive services. In this situation, accounting for grouping may be necessary when estimating the impact estimate’s standard error. This paper demonstrates that nonrandom sorting of individuals into groups can bias the standard error reported by common estimation approaches.
This paper provides a detailed discussion of the theory and practice of modern regression discontinuity. It describes how regression discontinuity analysis can provide valid and reliable estimates of general causal effects and of the specific effects of a particular treatment on outcomes for particular persons or groups.
This paper provides practical guidance for researchers who are designing studies that randomize groups to measure the impacts of educational interventions.
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.
This paper illustrates how to design an experimental sample for measuring the effects of educational programs when whole schools are randomized to a program and control group. It addresses such issues as what number of schools should be randomized, how many students per school are needed, and what is the best mix of program and control schools.
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