预测以微弱优势获胜:公布的民意调查中图形缩放对选民预测的影响

IF 1.8 3区 心理学 Q3 PSYCHOLOGY, APPLIED Journal of Behavioral Decision Making Pub Date : 2023-07-14 DOI:10.1002/bdm.2339
Edith Shalev, Eyal Peer
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引用次数: 0

摘要

选举调查结果的公开显示经常被操纵,以影响选民对竞选的预测。窄缩放就是这样一种操作,它包括截断图表的垂直轴,使其范围紧密地围绕条形图的值扩展。这种操作加剧了条之间的视觉差异,使边距看起来比无偏表示所显示的要大。目前的研究考察了描绘政治候选人支持程度的条形图的窄尺度是否会影响选民对选举结果的预测。在2022年美国州长和参议院选举期间进行的三项实验中,我们使用宽比例或窄比例的条形图向潜在选民展示了公布的民意调查结果。我们发现,当差距缩小时,选民更有可能预测民意调查中的领先候选人将赢得选举,并且以更大的优势获胜。尽管选民对小范围的民意调查持相对怀疑态度,但这种比例偏见仍然存在。我们进一步发现,当投票余量较大时,缩放效应减弱,当从图形显示中删除数值标签时,缩放效应增强。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Predicting a win by a small margin: The effect of graphic scaling in published polls on voters' predictions

The public display of election poll results is often manipulated to influence voter predictions about the race. Narrow scaling is one such manipulation that involves truncating the chart's vertical axis such that its range extends closely around the values of the bars. This manipulation exacerbates the visual difference between bars, making the margin appear larger than an unbiased representation would suggest. The current research examines whether narrow scaling of a bar chart depicting the degree of support for political candidate affects voters' predictions about election outcomes. In three experiments, conducted during the 2022 US gubernatorial and senate elections, we displayed published polls to potential voters using a wide- or a narrow-scaled bar chart. We found that when the scale is narrow, voters are more likely to predict that the leading candidate in the poll will win the election and by a larger margin. This scaling bias occurs despite voters' relative skepticism about narrow-scaled polls. We further find that the scaling effect is attenuated when the poll margin is relatively large and enhanced when numerical value labels are removed from the graphic display.

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来源期刊
CiteScore
4.40
自引率
5.00%
发文量
40
期刊介绍: The Journal of Behavioral Decision Making is a multidisciplinary journal with a broad base of content and style. It publishes original empirical reports, critical review papers, theoretical analyses and methodological contributions. The Journal also features book, software and decision aiding technique reviews, abstracts of important articles published elsewhere and teaching suggestions. The objective of the Journal is to present and stimulate behavioral research on decision making and to provide a forum for the evaluation of complementary, contrasting and conflicting perspectives. These perspectives include psychology, management science, sociology, political science and economics. Studies of behavioral decision making in naturalistic and applied settings are encouraged.
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Issue Information Do We Use Relatively Bad (Algorithmic) Advice? The Effects of Performance Feedback and Advice Representation on Advice Usage Evaluation of Extended Decision Outcomes Diffusion of Responsibility for Actions With Advice Dynamics of Reliance on Algorithmic Advice
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