Positive–Negative Asymmetry in the Evaluations of Political Candidates. The Role of Features of Similarity and Affect in Voter Behavior

Andrzej Falkowski , Magdalena Jabłońska

Abstract

In this study we followed the extension Tversky’s research on about features of similarity by with its application to open sets. Unlike the original closed-set model, in which a feature was shifted between a common and a distinctive set, we investigated how addition of new features and deletion of existing features affected similarity judgements. The model was tested empirically in a political context and we analyzed how positive and negative changes in a candidate’s profile affect the similarity of the politician to his or her ideal and opposite counterpart. The results showed a positive-negative asymmetry in comparison judgements, where enhancing negative features (distinctive for an ideal political candidate) had a greater effect on judgements than operations on positive (common) features. However, the effect was not observed for comparisons to a bad politician. Further analyses showed that in the case of a negative reference point, the relationship between similarity judgements and voting intention was mediated by the affective evaluation of the candidate.
Author Andrzej Falkowski (Wydział Psychologii)
Andrzej Falkowski,,
- Wydział Psychologii
, Magdalena Jabłońska (Wydział Psychologii)
Magdalena Jabłońska,,
- Wydział Psychologii
Journal seriesFrontiers in Psychology, ISSN 1664-1078, (A 35 pkt)
Issue year2018
No9:213
Pages1-12
Publication size in sheets0.55
ASJC Classification3200 General Psychology
DOIDOI:10.3389/fpsyg.2018.00213
URL https://www.frontiersin.org/article/10.3389/fpsyg.2018.00213
Languageen angielski
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fpsyg-09-00213.pdf 1.01 MB
Additional file
Falkowski, Oświadczenie_publ ( 1 )-1 (2).pdf 244.78 KB
Positive_negative Jablonska osw..pdf 36.6 KB
Score (nominal)35
Score sourcejournalList
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2018 = 1.03; WoS Impact Factor: 2017 = 2.089 (2) - 2017=2.749 (5)
Citation count*1 (2020-09-24)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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