Toward Causal Inferences in Discipline-Based Education Research: Using Regression Discontinuity Design to Understand the Effect of Classroom Interventions.

This paper overviews a quasi-experimental approach, the Regression Discontinuity (RD) design, as a viable tool to estimate the effects of classroom interventions in discipline-based education research (DBER). Classroom interventions have been widely used in undergraduate science, technology, engineering, and mathematics (STEM) instruction to improve student outcomes and promote educational equity. Yet two common approaches to access the impacts of these interventions on student outcomes, randomized control trials and covariate adjustment models, may not be an optimal choice when (1) it is not feasible or ethical to conduct randomized experiments, and (2) the instructor does not acquire sufficient student background characteristics to account for nonrandom assignments of students to the intervention. Fortunately, the RD designs exploit a predetermined intervention threshold and, under testable assumptions, can estimate the impact of an intervention by comparing students who narrowly qualified for the intervention to students who narrowly did not. Utilizing an extended example data from a real-world classroom intervention, we demonstrate why and how to perform RD analysis with classroom intervention data. We also provide step-by-step R Markdown (https://github.com/TheobaldLab/RegressionDiscontinuity.git) to encourage the implementation of the RD design in DBER.