Randomized control trials (RCTs) have long been considered the “gold standard” for evaluating the impacts of interventions. However, in most education RCTs, the sample of schools included is recruited based on convenience, potentially compromising a study’s ability to generalize to an intended population. An alternative approach is to recruit schools using a stratified recruitment method developed by Tipton. Until now, however, there has been limited information available about how to implement this approach in the field. In this article, we concretely illustrate each step of the stratified recruitment method in an evaluation of a college-level developmental algebra intervention. We reflect on the implementation of this process and conclude with five on-the-ground lessons regarding how to best implement this recruitment method in future studies.

中文翻译：

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通过改进招聘提高普遍性：从大规模随机试验中吸取的教训

长期以来，随机对照试验 (RCT) 一直被认为是评估干预措施影响的“金标准”。然而，在大多数教育 RCT 中，所包括的学校样本是根据便利性招募的，这可能会损害研究推广到目标人群的能力。另一种方法是使用蒂普顿开发的分层招聘方法来招聘学校。然而，到目前为止，关于如何在该领域实施这种方法的信息有限。在本文中，我们具体说明了分层招聘方法在大学水平发展代数干预评估中的每个步骤。我们反思了这一过程的实施，并总结了关于如何在未来的研究中最好地实施这种招聘方法的五个实地经验教训。