Personality goes a long way (for some). An experimental investigation into candidate personality traits, voters’ profile, and perceived likeability

Open Access
Authors
Publication date 03-2021
Journal Frontiers in Political Science
Article number 636745
Volume | Issue number 3
Number of pages 15
Organisations
  • Faculty of Social and Behavioural Sciences (FMG) - Amsterdam School of Communication Research (ASCoR)
Abstract
he personality traits of political candidates, and the way these are perceived by the public at large, matter for political representation and electoral behavior. Disentangling the effects of partisanship and perceived personality on candidate evaluations is however notoriously a tricky business, as voters tend to evaluate the personality of candidates based on their partisan preferences. In this article we tackle this issue via innovative experimental data. We present what is, to the best of our knowledge, the first study that manipulates the personality traits of a candidate and assesses its subsequent effects. The design, embedded in an online survey distributed to a convenience sample of US respondents (MTurk, N = 1,971), exposed respondents randomly to one of eight different “vignettes” presenting personality cues for a fictive candidate - one vignette for each of the five general traits (Big Five) and the three “nefarious” traits of the Dark Triad. Our results show that 1) the public at large dislikes “dark” politicians, and rate them significantly and substantially lower in likeability; 2) voters that themselves score higher on “dark” personality traits (narcissism, psychopathy, Machiavellianism) tend to like dark candidates, in such a way that the detrimental effect observed in general is completely reversed for them; 3) the effects of candidates’ personality traits are, in some cases, stronger for respondents displaying a weaker partisan attachment.
Document type Article
Note With supplementary files
Language English
Published at https://doi.org/10.3389/fpos.2021.636745
Other links https://osf.io/wxruy/
Downloads
Nai, Maier & Vranic 2021 (Frontiers) (Final published version)
Supplementary materials
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