Psychology research often focuses on interactions, and this has deep implications for inference from nonrepresentative samples. For the goal of estimating average treatment effects, we propose to fit a model allowing treatment to interact with background variables and then average over the distribution of these variables in the population. This can be seen as an extension of multilevel regression and poststratification (MRP), a method used in political science and other areas of survey research, where researchers wish to generalize from a sparse and possibly nonrepresentative sample to the general population. In this article, we discuss areas where this method can be used in the psychological sciences. We use our method to estimate the norming distribution for the Big Five Personality Scale using open source data. We argue that large open data sources like this and other collaborative data sources can potentially be combined with MRP to help resolve current challenges of generalizability and replication in psychology. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

中文翻译：

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了解您的人群并了解您的模型：使用基于模型的回归和后分层来概括观察样本之外的发现。

心理学研究通常侧重于相互作用，这对非代表性样本的推断具有深远的影响。为了估计平均治疗效果，我们建议拟合一个模型，允许治疗与背景变量相互作用，然后平均这些变量在人群中的分布。这可以看作是多级回归和后分层 (MRP) 的扩展，这是一种用于政治学和其他调查研究领域的方法，研究人员希望将稀疏且可能不具代表性的样本推广到普通人群。在本文中，我们讨论了这种方法可以在心理科学中使用的领域。我们使用我们的方法使用开源数据估计大五人格量表的规范分布。我们认为，像这样的大型开放数据源和其他协作数据源可以潜在地与 MRP 相结合，以帮助解决当前心理学中普遍性和复制的挑战。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）。