Regression discontinuity designs in a latent variable framework.

When randomized control trials are not available, regression discontinuity (RD) designs are a viable quasi-experimental method shown to be capable of producing causal estimates of how a program or intervention affects an outcome. While the RD design and many related methodological innovations came from the field of psychology, RDs are underutilized among psychologists even though many interventions are assigned on the basis of scores from common psychological measures, a situation tailor-made for RDs. In this tutorial, we present a straightforward way to implement an RD model as a structural equation model (SEM). By using SEM, we both situate RDs within a method commonly used in psychology, as well as show how RDs can be implemented in a way that allows one to account for measurement error and avoid measurement model misspecification, both of which often affect psychological measures. We begin with brief Monte Carlo simulation studies to examine the potential benefits of using a latent variable RD model, then transition to an applied example, replete with code and results. The aim of the study is to introduce RD to a broader audience in psychology, as well as show researchers already familiar with RD how employing an SEM framework can be beneficial. (PsycInfo Database Record (c) 2022 APA, all rights reserved).