This paper examines the properties of two nonexperimental study designs that can be used in educational evaluation: the comparative interrupted time series (CITS) design and the difference-in-difference (DD) design. The paper looks at the internal validity and precision of these two designs, using the example of the federal Reading First program as implemented in a midwestern state.
This paper presents a conceptual framework for designing and interpreting research on variation in program effects. The framework categorizes the sources of program effect variation and helps researchers integrate the study of variation in program effectiveness and program implementation.
This paper explores the use of instrumental variables analysis with a multisite randomized trial to estimate the effect of a mediating variable on an outcome.
Despite the growing popularity of the use of regression discontinuity analysis, there is only a limited amount of accessible information to guide researchers in the implementation of this research design. This paper provides an overview of the approach and, in easy-to-understand language, offers best practices and general guidance for practitioners.
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.
This paper provides practical guidance for researchers who are designing and analyzing studies that randomize schools — which comprise three levels of clustering (students in classrooms in schools) — to measure intervention effects on student academic outcomes when information on the middle level (classrooms) is missing.
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.