Regression discontinuity design (RDD) is a quasi-experimental design that utilizes a discontinuity or discontinuity in the variables of interest to assess the effects of an intervention. RDD is a method of causal inference used to determine the causal impact of a treatment or intervention. RDD is an observational design that enables the researcher to make causal inferences about the impact of a specific intervention without the need for randomization.
RDD is a powerful design for making causal estimates of treatment impacts by comparing outcomes at the discontinuity of a continuous treatment assignment rule. In an RDD, an individual or unit is assigned to treatment based on a selection rule which involves crossing a certain pre-specified threshold or cut-off point, such as a minimum score on a test, a certain age, or a certain date. Researchers identify the treatment effects by looking at the difference between those above and below the cutoff point.
RDD can be applied in a variety of real-world settings, including classrooms, health care settings, economics, government policies, and political campaigns. In educational research, RDD has been used to assess the effectiveness of interventions such as pre-kindergarten education, teacher training, and summer reading camps. In health care, RDD has been used to measure the impact of providing coverage through Medicaid on health care utilization, mortality, and health outcomes. In economics, RDD can be used to study the causal impact of tax policy changes on economic activity.
RDD is advantageous because it does not require randomization or a control population to make causal inferences. It thus allows for more efficient use of data and reduces the cost of a research project. In addition, RDD can provide more precise estimates of treatment effects because the discontinuity in treatment assignment is defined by specific criteria, as opposed to randomized treatment assignment.
Furthermore, RDD is preferable over other quasi-experimental designs such as difference-in-difference or regression discontinuity analysis. These designs are generally more difficult to implement because often times variables such as timing and duration must be taken into account. In comparison to these designs, RDD is quicker and simpler, making it the preferable strategy when the researcher wishes to assess the impact of an intervention.
In conclusion, while RDD is not as widely used as other quasi-experimental designs, it is nonetheless advantageous in many situations. RDD is a powerful strategy for making causal inferences and provides greater precision, cost efficiency, and ease of implementation compared to other quasi-experimentals designs. Therefore, researchers should consider utilizing RDD when they wish to assess the impact of an intervention in a real-world situation.
