Surveys provide important evidence for policymaking, decision-making, and understanding of society. However, conducting the large surveys required to provide subpopulation level estimates is expensive and time-consuming. Multilevel Regression and Poststratification (MRP) is a promising method to provide reliable estimates for subpopulations from surveys without the amount of data needed for reliable direct estimates. Graphical displays have been widely used to communicate and diagnose MRP estimates. However, there have been few studies on how visualisation should be performed in this field. Accordingly, this study examines the current practice of MRP visualisation using a systematic literature review. This study also applies MRP to estimate the Trump vote share in the U.S. 2016 presidential election using the Cooperative Congressional Election Study (CCES) data to illustrate the implication of current visualisation practices and explore alternatives for improvement. We find that uncertainty is not often displayed in the current practice, despite its importance for survey inference. The choropleth map is the most frequently used to display MRP estimates even though it only shows point estimates and could hinder the information conveyed. Using various graphical representations, we show that visualisation with uncertainty can illustrate the effect of different model specifications on the estimation result. In addition, this study also proposes a visualisation strategy to also take the bias-variance trade-off into account when evaluating MRP models.

中文翻译：

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可视化多级回归和后分层：当前实践的替代方案

调查为政策制定、决策和对社会的理解提供了重要的证据。然而，进行提供亚种群水平估计所需的大型调查既昂贵又耗时。多级回归和后分层 (MRP) 是一种很有前途的方法，可以通过调查为亚群提供可靠的估计，而无需可靠的直接估计所需的数据量。图形显示已广泛用于沟通和诊断 MRP 估算。然而，关于如何在该领域进行可视化的研究很少。因此，本研究使用系统的文献综述来检验 MRP 可视化的当前实践。本研究还应用 MRP 来估计特朗普在美国的投票份额 2016 年总统选举使用合作国会选举研究 (CCES) 数据来说明当前可视化实践的影响并探索改进的替代方案。尽管不确定性对调查推断很重要，但我们发现在当前实践中并不经常显示不确定性。等值线图是最常用于显示 MRP 估计值的地图，尽管它仅显示点估计值并且可能会阻碍所传达的信息。使用各种图形表示，我们表明具有不确定性的可视化可以说明不同模型规范对估计结果的影响。此外，本研究还提出了一种可视化策略，在评估 MRP 模型时也考虑了偏差-方差权衡。