Date of Graduation


Document Type


Degree Name

Doctor of Philosophy in Psychology (PhD)

Degree Level



Psychological Science


Scott Eidelman

Committee Member

Denise Beike

Second Committee Member

Ana Bridges


cognitive dissonance;elections;outcome-based dissonance;trivialization


The present study investigated cognitive dissonance theory—in particular, outcome-based dissonance, i.e., dissonance experienced from facing an outcome inconsistent with ones’ choice (in this case, preferring the candidate who lost the election)—in electoral contexts using the 2016 and 2020 American National Election Studies data (ns = 3,648 and 7,453, respectively). The particularly negative context of the 2016 and 2020 elections offered an opportunity to make a novel direct empirical comparison of choice valence between “hard/positive choices” (i.e., between two “good” alternatives) and “hard/negative choices” (i.e., between two “bad” alternatives) using real-world data. Results showed that after the election, respondents who preferred the winning candidate had more affectively polarized attitudes towards the candidates (i.e., rated them further apart, or more dissimilarly) as predicted by choice-based dissonance. By contrast, as predicted by outcome-based dissonance, respondents who preferred the losing candidate had less affectively polarized attitudes after the election (and even depolarized when it was an “easy” choice between one liked and one disliked candidate). Results also show a near inverse relationship between attitude change and polarization, offering the first empirical support of alternate modes of dissonance reduction in election contexts. This study demonstrates why future researchers must consider psychological processes beyond mere attitude change when investigating cognitive dissonance in order to understand the full psychological process of cognitive dissonance. Limitations and implications are discussed. All data, scripts, and online supplementary materials related to the present study are hosted on Open Science Framework ( and GitHub (