The Estimation of Voter Transitions in the 2015 British General Election: Combining Online Panels and Aggregate Data at the Constituency Level

IF 1.1 2区 社会学 Q4 SOCIAL SCIENCES, MATHEMATICAL METHODS
Paul W. Thurner, Ingrid Mauerer, Maxim Bort, André Klima, H. Küchenhoff
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引用次数: 0

Abstract

What have been the underlying voter shifts that led to the victory of the Con-servative Party in the 2015 British general election – against all predictions bypollsters? Analyses of voter transitions based on (online) surveys and recall ques-tions are plagued by sampling and response biases, whereas aggregate data analysesare suspect of the well-known ecological fallacy. We propose a systematic statisticalcombination of individual and aggregate data at the constituency level to identifyregional electoral shifts between the 2010 to 2015 British general elections, with aparticular focus on England. Large-scale individual data collected by the BritishElection Study Internet Panel (BESIP) allow us to locate more than 28,000 respon-dents in their constituencies. We estimate transitions based on a recently developedBayesian Hierarchical Hybrid Multinomial Dirichlet (HHMD) model. We discovera clear deviance from pure RxC ecological inference and from pure online panel-based estimations of transition matrices. Convergence diagnostics corroborate thesuperiority of the hybrid models.
2015年英国大选选民过渡的估计:结合选区层面的在线小组和汇总数据
导致保守党在2015年英国大选中获胜的潜在选民转变是什么?基于(在线)调查和召回问题对选民转变的分析受到抽样和反应偏差的困扰,而汇总数据分析则被怀疑是众所周知的生态谬误。我们建议对选区层面的个人和汇总数据进行系统的统计组合,以确定2010年至2015年英国大选期间的地区选举变化,特别关注英格兰。英国选举研究互联网小组(BESIP)收集的大规模个人数据使我们能够找到他们选区的28000多名受访者。我们基于最近开发的贝叶斯层次混合多项式Dirichlet(HHMD)模型来估计转换。我们发现了与纯RxC生态推断和纯在线基于面板的转移矩阵估计的明显偏差。收敛诊断证实了混合模型的优越性。
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来源期刊
Survey Research Methods
Survey Research Methods SOCIAL SCIENCES, MATHEMATICAL METHODS-
CiteScore
7.50
自引率
4.20%
发文量
0
审稿时长
52 weeks
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