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.

中文翻译：

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潜变量框架中的回归不连续性设计。

当随机对照试验不可用时，断点回归 (RD) 设计是一种可行的准实验方法，被证明能够对项目或干预措施如何影响结果进行因果估计。虽然 RD 设计和许多相关的方法创新来自心理学领域，但 RD 在心理学家中并未得到充分利用，尽管许多干预措施是根据常见心理测量的分数来分配的，这是为 RD 量身定制的情况。在本教程中，我们提出了一种将 RD 模型实现为结构方程模型 (SEM) 的简单方法。通过使用 SEM，我们既将 RD 置于心理学中常用的方法中，又展示了如何以一种允许人们考虑测量误差并避免测量模型错误指定的方式来实现 RD，这两者通常都会影响心理测量。我们从简短的蒙特卡洛模拟研究开始，检查使用潜变量 RD 模型的潜在好处，然后过渡到一个充满代码和结果的应用示例。该研究的目的是将 RD 介绍给更广泛的心理学受众，并向已经熟悉 RD 的研究人员展示使用 SEM 框架如何有益。