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Perceived closeness, ambiguity attitudes and voter turnout
Last registered on October 27, 2020

Pre-Trial

Trial Information
General Information
Title
Perceived closeness, ambiguity attitudes and voter turnout
RCT ID
AEARCTR-0006677
Initial registration date
October 26, 2020
Last updated
October 27, 2020 7:20 AM EDT
Location(s)
Region
Primary Investigator
Affiliation
Other Primary Investigator(s)
PI Affiliation
PI Affiliation
PI Affiliation
Additional Trial Information
Status
In development
Start date
2020-10-27
End date
2020-11-02
Secondary IDs
Abstract
We elicit voters’ beliefs about the closeness of the election and their ambiguity attitudes towards uncertainty of the outcomes of the 2020 presidential election in USA. We present different information to prime people’s perceived ambiguity of the election results. We provide the first test of the impact of pivotality in turnout by carefully controlling ambiguity attitudes at the same time.
External Link(s)
Registration Citation
Citation
Aydogan, Ilke et al. 2020. "Perceived closeness, ambiguity attitudes and voter turnout." AEA RCT Registry. October 27. https://doi.org/10.1257/rct.6677-1.1.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2020-10-27
Intervention End Date
2020-11-02
Primary Outcomes
Primary Outcomes (end points)
voting intentions and voting behavior
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We vary the perceived ambiguity of the margins of victory in the state that the respondent was registered to vote, between-subject. The idea is to frame the same polling results as being consistent or not, by manipulating the focus of respondents. Respondents see one of the following descriptions right after they have finished the screening questions and demographics for stratifying representative samples in each state.
• High-ambiguity Treatment: The candidate who wins enough states will be the winner of the upcoming presidential election. In many states, it is still uncertain which presidential candidate will be the likely winner. The polling data aggregator RealClearPolitics.com (https://www.realclearpolitics.com) publishes the results of all polls that are being conducted about the presidential race.
For the state of [Insert State], some polls show inconsistent results. The winner’s margins across these polls can differ a great deal.
• Low-ambiguity Treatment: The candidate who wins enough states will be the winner of the upcoming presidential election. In many states, it is still uncertain which presidential candidate will be the likely winner. The polling data aggregator RealClearPolitics.com (https://www.realclearpolitics.com) publishes the results of all polls that are being conducted about the presidential race.
For the state of [Insert State], many polls show consistent results. The winner’s margins across polls do not differ much.
Experimental Design Details
Randomization Method
randomization done by a random generator
Randomization Unit
randomize at the individual level
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
1000 respondents
Sample size: planned number of observations
1000 respondents
Sample size (or number of clusters) by treatment arms
500 respondents
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IESEG School of Management
IRB Approval Date
2020-10-27
IRB Approval Number
2020-01
Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)
REPORTS & OTHER MATERIALS