The geographic distribution of psychological constructs has long been an area of focus for psychological researchers. Recently, however, there has been increased interest in investigations of the so-called subnational distribution of psychological variables, which focus on localized groupings of individuals within spatial units, such as counties or states. By estimating the subnational distribution of a given outcome (e.g., estimating its state- or county-level means), researchers have been able to address questions about the spatial variation of a variety of psychological constructs and investigate the regional association between psychological phenomena and real-world outcomes, such as health outcomes, prosocial behavior, and racial inequity. Unfortunately, however, there are many challenges to estimating a construct's subnational distribution, such as those raised by response biases and subnational sparsity. To help psychological researchers address these issues, we provide a comprehensive discussion of subnational estimation and introduce multilevel regression and poststratification (MrP), a method that is widely considered to be the gold standard for subnational estimation with random samples. As psychologists often do not have access to large, national random samples, we also report 3 studies evaluating MrP's performance under simulated and real-world conditions of sample biases. Ultimately, we find that MrP is likely to outperform the subnational estimation methods that psychological researchers currently use. Based on this, we suggest that psychologists interested in understanding how psychological phenomena vary below the nation level use MrP to conduct these investigations. To help facilitate this, we have made all code and data used for the reported studies publicly available. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

中文翻译：

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大，小，丑：心理数据有缺陷的地理估计。

心理构造的地理分布长期以来一直是心理学研究人员关注的领域。然而，近来，人们对所谓的心理变量的次国家级分布的研究越来越感兴趣，这种研究集中于空间单位（例如县或州）内个人的局部分组。通过估计给定结果的次国家级分布（例如，估计其州或县级均值），研究人员已经能够解决有关各种心理结构的空间变化的问题，并研究心理现象与真实状态之间的区域关联。世界的结果，例如健康结果，亲社会行为和种族不平等。不幸的是，然而，估计结构有很多挑战。的地区分布，例如因回应偏见和地区稀疏而引起的分布。为了帮助心理研究人员解决这些问题，我们对地方估计进行了全面的讨论，并介绍了多层次回归和后分层（MrP），该方法被广泛认为是使用随机样本进行地方估计的金标准。由于心理学家通常无法获得大量的全国性随机样本，因此我们还报告了3项评估MrP在模拟和真实样本偏差条件下的性能的研究。最终，我们发现MrP可能胜过心理学研究人员当前使用的地方评估方法。基于此，我们建议有兴趣了解低于国家水平的心理现象变化的心理学家请使用MrP进行这些调查。为帮助实现此目的，我们已公开提供了用于报告研究的所有代码和数据。（PsycINFO数据库记录（c）2019 APA，保留所有权利）。