2026-10-12T19:04:37Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33721570 2026-09-14T08:26:55Z category_30409 category_30034 item_type_3 month_year_09_2026

2024 US election and Populism: dataset and analyses reported in paper. Andrea Matos Devesa (24794269) Merrisa Lin (24870502) Ying-yi Hong (24870504) Social psychology Social and political philosophy populism latent profile analysis 2024 US election anti-immigration anti-elite sentiment <p dir="ltr">We proposed to untangle populism across the political spectrum, and how it may manifest in diverging ways, using a person-driven approach. We took advantage of the 2024 U.S. election to investigate how factors related to political and economic dissatisfaction may manifest in different “types” of populism, and how these types may predict different outcomes – specifically, anti-immigration and anti-elite attitudes. Given the highly polarized atmosphere during the 2024 election, culminating in the re-election of Donald Trump, we believed the 2024 election provided an apt and timely context to study populism. We used a latent profile analysis (LPA), a person-centric approach based on theory-grounded selected indicators (Spurk et al., 2020; Bauer & Curran, 2004) to do so. Rather than assuming voters “adhere to coherent ideologies that can be identified a priori” (Bonikowski & DiMaggio, 2016, p.951), an LPA allowed us to identify hidden subgroups of voters with varying political attitudes that do not cleanly match onto the traditional left-right political binary. This is especially crucial when studying populism, given the “diagonalism” – the mobilization and loose alliances of different groups <i>across</i> the left-right spectrum – of a lot of modern populist discourse (Callison & Slobodian, 2021; Klein, 2023).</p><p dir="ltr">The cleaned dataset and all the analyses reported in the paper can be found here.</p> 2026-09-14T08:26:55Z Dataset Dataset 10.6084/m9.figshare.33721570.v1 https://figshare.com/articles/dataset/2024_US_election_and_Populism_dataset_and_analyses_reported_in_paper_/33721570 CC BY 4.0