Instrumental variables is a popular approach for causal inference in education when randomization of treatment is not feasible. Using a first-year college program as a running example, this article reviews the five assumptions that must be met to successfully use instrumental variables to estimate a causal effect with observational data: SUTVA,…

Temporality-driven covariate classification had limited impact on: the specification of directed acyclic graphs (DAGs) by 85 novice analysts (medical undergraduates); or the risk of bias in DAG-informed multivariable models designed to generate causal inference from observational data. Only 71 students (83.5%) managed to complete the…

The nature of students' ideas about the scientific practices used by astronomers when studying objects in our Solar System is of widespread interest to discipline-based astronomy education researchers. A sample of middle-school, high-school, and college students (N = 42) in the U.S. were interviewed about how astronomers were able to learn about…