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24.08.2018 | Original Paper

# Anxious Voters in the 2016 U.S. Election: An Analysis of How They Decided from the ERPC2016

verfasst von: James E. Monogan III

Erschienen in: Political Behavior | Ausgabe 1/2020

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## Abstract

How did anxiety influence vote choice in the 2016 U.S. presidential election, when both major candidates faced substantial voter disapproval? Drawing from the model of affective intelligence (Marcus et al. 2000), I argue that anxious voters will base their vote choice more strongly on issues and candidate qualities and less on partisanship relative to non-anxious voters. This study uses a registered report as part of the 2016 Election Research Preacceptance Competition, wherein a complete analysis plan was submitted for review prior to the 2016 American National Election Study’s release. In so doing, it serves as a model for other preregistered and potentially preaccepted research. Consistent with past findings, this study shows that anxious voters in 2016 were less likely to rely on past partisanship and more likely to base decisions on personal qualities. However, anxiety did not condition the effect of issues in the election. Further, Donald Trump faced no more anxiety among Republican voters than Mitt Romney did in 2012 due to a rise of in-party enthusiasm. The non-conditional effect of issues and a relatively narrow gap in partisan anxiety served as unique features of the 2016 election.
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Fußnoten
1
Based on ABC News/Washington Post polls over more than 30 years, as reported by USA Today.

2
Figure A.5 in the online appendix shows results for the model presented in this paper, repeated for each presidential election from 1984-2016 using the American National Election Study cumulative data file. That figure shows interaction effects of anxiety with partisanship, issue proximity, and candidate personal qualities. As the figure there shows, the interaction term between partisanship and anxiety takes as sharp negative turn in 2012 and 2016 relative to 1984–2008.

4

5
The originally reported version of the 2012 model actually had a coding error in it due to a mistake in specifying a Boolean statement. Hence, the 2012 results reported now are different from what the reviewers originally saw. This correction was presented to the reviewers as a coding error from the preregistered design for their consideration, though the corrected version exactly fits the substantive specification from the preregistration. This illustrates how human errors can be addressed in registered reports.

6
Wallace (2016) cites a report that 92% of presidential campaign ads were either negative or contrast ads during the stretch from November 1–5, 2016 when 69,500 ads were observed on the air.

7
An alternative spatial model of politics is directional theory, which maintains that voters prefer candidates who are on the same side of an issue as they are and can be stimulated to support candidates taking strong positions on the issue (Rabinowitz and Macdonald 1989). In fact, Pardos-Prado and Dinas (2010) show that directional theory is particularly apt to explain voter behavior in polarized party systems, which certainly would characterize the United States in 2016. Regardless of whether the proximity or directional model best captures the effect of issues, either model would be more applicable among nervous voters who would be more prone to consider issues as part of their choice than complacent voters would. In fact, Table A.2 in the appendix replicates the main model for 2016, but replaces the proximity model of voter utility with the directional model. The results for the role of issues when conditioning on anxiety are similar.

8
For example, Alvarez and Nagler (1995) showed that in 1992 voters who felt the economy had declined under George H.W. Bush were less likely to vote for Bush and more likely to vote for Clinton (with independent Ross Perot seeing no real gain on this consideration).

9
Lewis-Beck et al. (2008) observe that candidate characteristics are more likely to influence vote choices among independent partisan leaners and weak partisans than they are for strong partisans. For this reason, the model of Table 4 was replicated on subsamples of independent leaners, weak partisans, and strong partisans. Much like prior findings, weak partisans were the most responsive to candidate qualities.

10
As an alternative to absolute distance, I estimated a model using squared Euclidean distance and present the results in appendix Table A.1. Also, when using an individual’s self placement and that same person’s placement of the candidates, a projection bias can emerge in which how a respondent places the candidates can reflect how the voter already feels about the candidates. To address this issue, I estimated a model in which the position of each candidate was fixed for all voters at the overall mean rating of the candidate, and issue distance was measured with the absolute distance between self-placement and the candidate mean. This model is presented in Table A.3. The results are substantively similar in these two alternative models.

11
For the 2016 Trump v. Clinton data, Cronbach’s $$\alpha$$ on the six issue proximity scores was 0.93, indicating high reliability. Computed with the psych package version 1.7.8 in R 3.4.3.

12
Hope and proud were multiplied by -1 so that higher values would correspond to greater anxiety. Anger, afraid, and disgust were left on their original scale.

13
The Bayesian measurement model follows the same logic as confirmatory factor analysis (Bollen 1989).

14
Factor score estimates are posterior means of each respondent’s score, based on 150,000 MCMC samples after a 150,000 burn-in (3 chains of 50,000 each in both cases). There are 3854 factor scores, so it proved difficult to record all of the scores for as many iterations as the model parameters were recorded.

15
In the online appendix, Table A.7 presents an alternate version of the 2016 model that excludes disgust, for the sake of comparability with the 2012 model. The results of Appendix Table A.7 are substantively similar to those presented in Table 4 here in the main text.

16
A two-tailed difference-of-means test yielded $$t_{2129.2 df} = 7.7783$$ ($$p < 0.0001$$).

17
$$t_{1520.8 df} = 3.5889$$ ($$p = 0.0003$$).

18
$$t_{1703.5 df} = 2.5323$$ ($$p = 0.0114$$).

19
In fact, the average dropped insignificantly from 0.526 to 0.520. $$t_{1588.2 df} = 0.6348$$ ($$p = 0.5257$$).

20
Appendix Figs. A.1 and A.2 show the marginal density plot for each interaction term for the 2012 and 2016 models, respectively. The figures show more detail about these coefficients’ distributions and robustness.

21
Figures 3 and 4 show predicted probabilities for 2012 and 2016, respectively. The figures feature color but were designed to be grayscale friendly. The online appendix includes alternative versions, called Figs. A.3 and A.4, which rely heavily on color to present 90% credible intervals around the predicted probabilities. Figures A.3 and A.4 also differ in that, instead of using a standard deviation above and below the mean of anxiety, the predicted probabilities are computed for the minimum, mean, and maximum levels of anxiety—which is actually what the preregistered design calls for. The preregistered versions are online because the wider spread in anxiety reduces credible interval overlap, making the color-required figures easier to read.

22
Only if anxiety were to exceed a score of 0.988 would partisanship’s mean coefficient be negative on account of the negative interaction effect of anxiety with partisanship. However, 0.988 exceeds the maximum observed value of anxiety, so the effect is always positive. The other two interaction terms have positive mean coefficients, so the effects of issue proximity and candidate qualities certainly are always positive.

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Titel
Anxious Voters in the 2016 U.S. Election: An Analysis of How They Decided from the ERPC2016
verfasst von
James E. Monogan III
Publikationsdatum
24.08.2018
Verlag
Springer US
Erschienen in
Political Behavior / Ausgabe 1/2020
Print ISSN: 0190-9320
Elektronische ISSN: 1573-6687
DOI
https://doi.org/10.1007/s11109-018-9491-3

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