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Inferring the dynamics of rising radical right-wing party support using Gaussian processes
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Applied Mathematics and Statistics.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Applied Mathematics and Statistics.ORCID iD: 0000-0002-1436-9103
Univ Leeds, Sch Math, Dept Stat, Leeds, W Yorkshire, England;Alan Turing Inst, London, England.ORCID iD: 0000-0003-0701-1274
2019 (English)In: Philosophical Transactions. Series A: Mathematical, physical, and engineering science, ISSN 1364-503X, E-ISSN 1471-2962, Vol. 377, no 2160, article id 20190145Article in journal (Refereed) Published
Abstract [en]

The use of classical regression techniques in social science can prevent the discovery of complex, nonlinear mechanisms and often relies too heavily on both the expertise and prior expectations of the data analyst. In this paper, we present a regression methodology that combines the interpretability of traditional, well used, statistical methods with the full predictability and flexibility of Bayesian statistics techniques. Our modelling approach allows us to find and explain the mechanisms behind the rise of Radical Right-wing Populist parties (RRPs) that we would have been unable to find using traditional methods. Using Swedish municipality-level data (2002-2018), we find no evidence that the proportion of foreign-born residents is predictive of increases in RRP support. Instead, education levels and population density are the significant variables that impact the change in support for the RRP, in addition to spatial and temporal control variables. We argue that our methodology, which produces models with considerably better fit of the complexity and nonlinearities often found in social systems, provides a better tool for hypothesis testing and exploration of theories about RRPs and other social movements. This article is part of the theme issue 'Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences'.

Place, publisher, year, edition, pages
ROYAL SOC , 2019. Vol. 377, no 2160, article id 20190145
Keywords [en]
Gaussian processes, coupling functions, Radical Right-wing parties, Bayesian statistics
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-406505DOI: 10.1098/rsta.2019.0145ISI: 000511612600013PubMedID: 31656139OAI: oai:DiVA.org:uu-406505DiVA, id: diva2:1413640
Available from: 2020-03-10 Created: 2020-03-10 Last updated: 2020-03-10Bibliographically approved

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Sumpter, David J. T.

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