scholarly journals Social Desirability Bias in the 2016 Presidential Election

The Forum ◽  
2016 ◽  
Vol 14 (4) ◽  
Author(s):  
Samara Klar ◽  
Christopher R. Weber ◽  
Yanna Krupnikov
Keyword(s):  
Social Context ◽  
Social Norms ◽  
Social Desirability ◽  
Presidential Election ◽  
Social Desirability Bias ◽  
Donald Trump ◽  
Self Monitoring ◽  
Psychological Trait

AbstractPartisanship is a stable trait but expressions of partisan preferences can vary according to social context. When particular preferences become socially undesirable, some individuals refrain from expressing them in public, even in relatively anonymous settings such as surveys and polls. In this study, we rely on the psychological trait of self-monitoring to show that Americans who are more likely to adjust their behaviors to comply with social norms (i.e. high self-monitors) were less likely to express support for Donald Trump during the 2016 Presidential Election. In turn, as self-monitoring decreases, we find that the tendency to express support for Trump increases. This study suggests that – at least for some individuals – there may have been a tendency in 2016 to repress expressed support for Donald Trump in order to mask socially undesirable attitudes.

Global Social Norms and Environmental Behavior

2017 ◽  
Vol 50 (3) ◽  
pp. 247-272 ◽  
Author(s):  
Stepan Vesely ◽  
Christian A. Klöckner
Keyword(s):  
Social Norms ◽  
Social Desirability ◽  
Environmental Effect ◽  
Environmental Action ◽  
Social Desirability Bias ◽  
Elicitation Method ◽  
Environmental Behaviors ◽  
Shared Beliefs ◽  
Wide Range ◽  
Out Of Sample

We adopt a recently introduced incentivized method to elicit widely shared beliefs concerning (a) social norms, (b) environmental effect, and (c) difficulty of a wide range of environmental behaviors. We establish that these characteristics, as reflected in elicited beliefs recorded in one sample, predict (out-of-sample) environmental behaviors in a second separate sample. Pro-environmental behaviors perceived to be more socially appropriate and easier to perform, in particular, are more likely to be chosen. We show that subjective social norms mediate the effect of “global” (widely shared) social norms on behavior, which improves our understanding of the normative processes underlying pro-environmental action. Our use of an incentivized elicitation method might moreover mitigate problems associated with conventional surveys, such as social desirability bias, consistency bias, and inattentive responding, as discussed in the article.

Did Shy Trump Supporters Bias the 2016 Polls? Evidence from a Nationally-representative List Experiment

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Alexander Coppock
Keyword(s):  
Social Desirability ◽  
Presidential Election ◽  
Social Desirability Bias ◽  
Representative Survey ◽  
Direct Question ◽  
Vote Preference ◽  
List Experiment ◽  
Nationally Representative

AbstractExplanations for the failure to predict Donald Trump’s win in the 2016 Presidential election sometimes include the “Shy Trump Supporter” hypothesis, according to which some Trump supporters succumb to social desirability bias and hide their vote preference from pollsters. I evaluate this hypothesis by comparing direct question and list experimental estimates of Trump support in a nationally representative survey of 5290 American adults fielded from September 2 to September 13, 2016. Of these, 32.5% report supporting Trump’s candidacy. A list experiment conducted on the same respondents yields an estimate 29.6%, suggesting that Trump’s poll numbers were not artificially deflated by social desirability bias as the list experiment estimate is actually lower than direct question estimate. I further investigate differences across measurement modes for relevant demographic and political subgroups and find no evidence in support of the “Shy Trump Supporter” hypothesis.

Social desirability bias in self-monitoring data

1980 ◽  
Vol 2 (4) ◽  
pp. 239-247 ◽  
Author(s):  
Janice Kiecolt-Glaser ◽  
Jo Ann Murray
Keyword(s):  
Social Desirability ◽  
Monitoring Data ◽  
Social Desirability Bias ◽  
Self Monitoring

Examining false beliefs about voter fraud in the wake of the 2020 Presidential Election

2021 ◽  
Author(s):  
Gordon Pennycook ◽  
David Gertler Rand
Keyword(s):  
Presidential Election ◽  
False Beliefs ◽  
Wide Spread ◽  
Donald Trump ◽  
Legal Challenges ◽  
Election Fraud ◽  
Voter Fraud ◽  
Election News

The 2020 U.S. Presidential Election saw an unprecedented number of false claims alleging election fraud and arguing that Donald Trump was the actual winner of the election. Here we report a survey exploring belief in these false claims that was conducted three days after Biden was declared the winner. We find that a majority of Trump voters in our sample – particularly those who were more politically knowl-edgeable and more closely following election news – falsely believed that election fraud was wide-spread and that Trump won the election. Thus, false beliefs about the election are not merely a fringe phenomenon. We also find that Trump conceding or losing his legal challenges would likely lead a ma-jority of Trump voters to accept Biden’s victory as legitimate, although 40% said they would continue to view Biden as illegitimate regardless. Finally, we found that levels of partisan spite and endorsement of violence were equivalent between Trump and Biden voters.

Collecting High-Quality Data

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Social Desirability ◽  
Measurement Errors ◽  
Monitoring And Evaluation ◽  
Quality Data ◽  
Social Desirability Bias ◽  
High Quality ◽  
High Quality Data ◽  
Evaluation Systems ◽  
Demand Effect

Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.

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