Abstract

Given the increasing attention ambivalence is receiving from the psychological community, it must be asked if pollsters' (routinely) dichotomous political opinion surveys are missing something crucial. To determine if there is any legitimacy to this question, undergraduates attending a Liberal Arts college in Southern California were asked to rate their level of agreement/disagreement to 28 statements regarding President Trump in two studies, with the items drawn from actual Quinnipiac (Q) and Brookings Institute (BI) surveys. To quantify ambivalence participants were told they could mark one or two responses per item, with double-responses serving as a measure of ambivalence. In Study 1, mean Trump approval ratings divided along party lines, and were consistent with the Q and BI findings. Nonetheless, approximately 40% of participants registered some level of ambivalence across all political-party affiliations, with those defining themselves as Neither Democrats (DEMs) nor Republicans (REPs) showing the greatest degree of ambivalence. In Study 2, ambivalence towards President Trump was examined looking at both party affiliation and political ideology (Conservative, Moderate, and Liberal). Again, roughly 40% of participants displayed some level of ambivalence, with greater degrees of ambivalence for Independents relative to DEMs and REPs, and Moderates relative to Liberals. Given research indicating that ambivalence is associated with delayed decision making and decisions based on "in the moment" contextual information, our findings our suggestive: if political opinion pollsters do not assess ambivalence, they may be missing information on a fair-sized demographic that could influence an election based on negative information (real or fictitious) surfacing only days before an election… as it did in 2016.

Highlights

  • Since the 2016 surprise election of Donald Trump as President of the United States, much has been written regarding the failure of political opinion polls to reliably forecast the potentiality of that outcome

  • The ρJJ(k) are the diagonal elements of the 7x7 density matrix for each participant k, and (J−4) corresponds to the interval-scale number assigned to each ordinal category

  • Error bars in the figure correspond to 95% confidence intervals (CIs) of the mean [23]

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Summary

Introduction

Since the 2016 surprise election of Donald Trump as President of the United States, much has been written regarding the failure of political opinion polls to reliably forecast the potentiality of that outcome.

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