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.
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.
Strategies for Interpreting and Reporting Intervention Effects on Subgroups
This revised paper examines strategies for interpreting and reporting estimates of intervention effects for subgroups of a study sample. Specifically, the paper considers: why and how subgroup findings are important for applied research, the importance of prespecifying subgroups before analyses are conducted, and the importance of using existing theory and prior research to distinguish between subgroups for which study findings are confirmatory, as opposed to exploratory.
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.