Common election poll reporting is often misleading as sample uncertainty is addressed insufficiently or not covered at all. Furthermore, main interest usually lies beyond the simple party shares. For a more comprehensive opinion poll and election coverage, we propose shifting the focus toward the reporting of survey-based probabilities for specific events of interest. We present such an approach for multi-party electoral systems, focusing on probabilities of coalition majorities. A Monte Carlo approach based on a Bayesian Multinomial-Dirichlet model is used for estimation. Probabilities are estimated, assuming the election was held today (“now-cast”), not accounting for potential shifts in the electorate until election day (“fore-cast”). Since our method is based on the posterior distribution of party shares, the approach can be used to answer a variety of questions related to the outcome of an election. We also introduce visualization techniques that facilitate a more adequate depiction of relevant quantities as well as respective uncertainties. The benefits of our approach are discussed by application to the German federal elections in 2013 and 2017. An open-source implementation of our methods is freely available in the R package coalitions.

中文翻译：

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KOALA：选举报道的新范式

常见的选举民意测验报告往往会产生误导，因为样本不确定性未得到充分解决或根本没有涵盖。此外，主要兴趣通常超出了简单的党派份额。为了获得更全面的民意测验和选举报道，我们建议将重点转移到针对特定感兴趣事件的基于调查的概率的报告上。我们针对多党选举制度提出这种方法，重点是联盟多数派的可能性。基于贝叶斯多项式-Dirichlet模型的蒙特卡洛方法用于估计。假定今天（今天）举行选举，则估计概率，不考虑选民直到选举日之前的潜在变动（“预先选举”）。由于我们的方法基于党派股份的后验分布，该方法可用于回答与选举结果有关的各种问题。我们还介绍了可视化技术，可帮助更充分地描述相关数量以及各自的不确定性。我们的方法的优势已通过在2013年和2017年德国联邦选举中的应用进行了讨论。我们方法的开源实现可在R包联盟。