3 datasets found
  1. d

    Replication data for: Forecasts of the 2012 U.S. presidential election based...

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 20, 2023
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    Graefe, Andreas; Armstrong, J. Scott (2023). Replication data for: Forecasts of the 2012 U.S. presidential election based on candidates’ perceived competence in handling the most important issue [Dataset]. http://doi.org/10.7910/DVN/22949
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    Dataset updated
    Nov 20, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Graefe, Andreas; Armstrong, J. Scott
    Area covered
    United States
    Description

    The Big-Issue Model predicts election outcomes based on voters’ perceptions of candidates’ ability to handle the most important issue. The model provided accurate forecasts of the 2012 U.S. presidential election. The results demonstrate the usefulness of the model in situations where one issue clearly dominates the campaign, such as the state of the economy in the 2012 election. In addition, the model is particularly valuable if economic fundamentals disagree, a situation in which forecasts from traditional political economy models suggest high uncertainty. The model provides immediate feedback to political candidates and parties on the success of their campaign and can advise them on which issues to assign the highest priority.

  2. H

    Replication data for: Accuracy of combined forecasts for the 2012...

    • dataverse.harvard.edu
    Updated Dec 5, 2013
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    Andreas Graefe (2013). Replication data for: Accuracy of combined forecasts for the 2012 Presidential Election: The PollyVote [Dataset]. http://doi.org/10.7910/DVN/POLLYVOTE2012
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 5, 2013
    Dataset provided by
    Harvard Dataverse
    Authors
    Andreas Graefe
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    We review the performance of the PollyVote, which combined forecasts from polls, prediction markets, experts’ judgment, political economy models, and index models to forecast the two-party popular vote in the 2012 U.S. Presidential Election. Throughout the election year the PollyVote provided highly accurate forecasts, outperforming each of its component methods, as well as the forecasts from FiveThirtyEight.com. Gains in accuracy were particularly large early in the campaign, when uncertainty about the election outcome is typically high. The results confirm prior research showing that combining is one of the most effective approaches to generating accurate forecasts.

  3. A

    ‘US non-voters poll data’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jan 28, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘US non-voters poll data’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-us-non-voters-poll-data-782f/496780e9/?iid=032-799&v=presentation
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    Dataset updated
    Jan 28, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    United States
    Description

    Analysis of ‘US non-voters poll data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/us-non-voters-poll-datae on 28 January 2022.

    --- Dataset description provided by original source is as follows ---

    About this dataset

    This dataset contains the data behind Why Many Americans Don't Vote.

    Data presented here comes from polling done by Ipsos for FiveThirtyEight, using Ipsos’s KnowledgePanel, a probability-based online panel that is recruited to be representative of the U.S. population. The poll was conducted from Sept. 15 to Sept. 25 among a sample of U.S. citizens that oversampled young, Black and Hispanic respondents, with 8,327 respondents, and was weighted according to general population benchmarks for U.S. citizens from the U.S. Census Bureau’s Current Population Survey March 2019 Supplement. The voter file company Aristotle then matched respondents to a voter file to more accurately understand their voting history using the panelist’s first name, last name, zip code, and eight characters of their address, using the National Change of Address program if applicable. Sixty-four percent of the sample (5,355 respondents) matched, although we also included respondents who did not match the voter file but described themselves as voting “rarely” or “never” in our survey, so as to avoid underrepresenting nonvoters, who are less likely to be included in the voter file to begin with. We dropped respondents who were only eligible to vote in three elections or fewer. We defined those who almost always vote as those who voted in all (or all but one) of the national elections (presidential and midterm) they were eligible to vote in since 2000; those who vote sometimes as those who voted in at least two elections, but fewer than all the elections they were eligible to vote in (or all but one); and those who rarely or never vote as those who voted in no elections, or just one.

    The data included here is the final sample we used: 5,239 respondents who matched to the voter file and whose verified vote history we have, and 597 respondents who did not match to the voter file and described themselves as voting "rarely" or "never," all of whom have been eligible for at least 4 elections.

    If you find this information useful, please let us know.

    License: Creative Commons Attribution 4.0 International License

    Source: https://github.com/fivethirtyeight/data/tree/master/non-voters

    This dataset was created by data.world's Admin and contains around 6000 samples along with Race, Q27 6, technical information and other features such as: - Q4 6 - Q8 3 - and more.

    How to use this dataset

    • Analyze Q10 3 in relation to Q8 6
    • Study the influence of Q6 on Q10 4
    • More datasets

    Acknowledgements

    If you use this dataset in your research, please credit data.world's Admin

    Start A New Notebook!

    --- Original source retains full ownership of the source dataset ---

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Click to copy link
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Graefe, Andreas; Armstrong, J. Scott (2023). Replication data for: Forecasts of the 2012 U.S. presidential election based on candidates’ perceived competence in handling the most important issue [Dataset]. http://doi.org/10.7910/DVN/22949

Replication data for: Forecasts of the 2012 U.S. presidential election based on candidates’ perceived competence in handling the most important issue

Related Article
Explore at:
Dataset updated
Nov 20, 2023
Dataset provided by
Harvard Dataverse
Authors
Graefe, Andreas; Armstrong, J. Scott
Area covered
United States
Description

The Big-Issue Model predicts election outcomes based on voters’ perceptions of candidates’ ability to handle the most important issue. The model provided accurate forecasts of the 2012 U.S. presidential election. The results demonstrate the usefulness of the model in situations where one issue clearly dominates the campaign, such as the state of the economy in the 2012 election. In addition, the model is particularly valuable if economic fundamentals disagree, a situation in which forecasts from traditional political economy models suggest high uncertainty. The model provides immediate feedback to political candidates and parties on the success of their campaign and can advise them on which issues to assign the highest priority.

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