We address the task of determining, from statistical averages alone, whether a population under study consists of several subpopulations, unknown to the investigator, each responding to a given treatment markedly differently. We show that such determination is feasible in three cases: (1) randomized trials with binary treatments, (2) models where…

When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This…

Objective: Patient education is key to the management of acute and chronic conditions. However, the majority of such educational interventions have been reported from health-care settings. In contrast, this study aims to evaluate whether a home-based intervention can result in better understanding about type 2 diabetes mellitus and can increase…

The current study aimed to investigate the effectiveness of computerized instructional packages on concept acquisition and improving academic achievement among deaf students in Saudi Arabia. The sample consisted of (16) third-grade female deaf students in prep stage for the first semester of the academic year 2013/2014, randomly selected from…

Randomized controlled trials (RCTs) and regression discontinuity (RD) studies both provide estimates of causal effects. A major difference between the two is that RD only estimates local average treatment effects (LATE) near the cutoff point of the forcing variable. This has been cited as a drawback to RD designs (Cook & Wong, 2008).…