86 datasets found
  1. U.S. online dating service users lying on their profiles 2024

    • statista.com
    Updated Jul 22, 2024
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    Statista (2024). U.S. online dating service users lying on their profiles 2024 [Dataset]. https://www.statista.com/statistics/1481187/us-online-dating-users-lying-on-profiles/
    Explore at:
    Dataset updated
    Jul 22, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 27, 2024 - Apr 1, 2024
    Area covered
    United States
    Description

    According to a survey conducted in April 2024 in the United States, one in five online dating service users had lied about their age on their dating profile, while 14 percent had lied about their income. A further 14 percent had lied about their hobbies and interests, and 12 percent had lied about their height.

  2. Frequency of lying and cheating in the United States 2016

    • statista.com
    Updated Apr 4, 2016
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    Statista (2016). Frequency of lying and cheating in the United States 2016 [Dataset]. https://www.statista.com/statistics/539802/us-frequency-of-lying/
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    Dataset updated
    Apr 4, 2016
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Feb 9, 2016 - Feb 10, 2016
    Area covered
    United States
    Description

    This statistic shows the results of a survey among adult Americans in 2016 on how often they feel the need to lie or cheat. During the survey, 13 percent of respondents stated they occasionally have to lie or to cheat.

  3. o

    Replication data for: Lying Aversion and the Size of the Lie

    • openicpsr.org
    Updated Feb 1, 2018
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    Uri Gneezy; Agne Kajackaite; Joel Sobel (2018). Replication data for: Lying Aversion and the Size of the Lie [Dataset]. http://doi.org/10.3886/E113165V1
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    Dataset updated
    Feb 1, 2018
    Dataset provided by
    American Economic Association
    Authors
    Uri Gneezy; Agne Kajackaite; Joel Sobel
    Description

    This paper studies lying. An agent randomly picks a number from a known distribution. She can then report any number and receive a monetary payoff based only on her report. The paper presents a model of lying costs that generates hypotheses regarding behavior. In an experiment, we find that the highest fraction of lies is from reporting the maximal outcome, but some participants do not make the maximal lie. More participants lie partially when the experimenter cannot observe their outcomes than when the experimenter can verify the observed outcome. Partial lying increases when the prior probability of the highest outcome decreases.

  4. Survey on lies and misuse of facts in politics and media in Europe 2018, by...

    • statista.com
    Updated Jan 24, 2025
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    Statista (2025). Survey on lies and misuse of facts in politics and media in Europe 2018, by country [Dataset]. https://www.statista.com/statistics/913922/fact-misuse-in-politics-and-media-in-europe/
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    Dataset updated
    Jan 24, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jun 22, 2018 - Jul 6, 2018
    Area covered
    Europe
    Description

    This statistic illustrates the results of a survey regarding the public opinion on the amount of lying and misuse of facts in politics and media compared to 30 years ago in selected countries in Europe in 2018. According to data published by IPSOS, 61 percent of Turkish respondents thought that the amount of lying and misuse of facts in politics and the media had increased compared to 30 years ago.

  5. o

    Replication data for: On lies and hard truths

    • openicpsr.org
    Updated Jun 17, 2021
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    Sascha Behnk; Ernesto Reuben (2021). Replication data for: On lies and hard truths [Dataset]. http://doi.org/10.3886/E143161V1
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    Dataset updated
    Jun 17, 2021
    Authors
    Sascha Behnk; Ernesto Reuben
    License

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

    Description

    We run an experimental study using sender-receiver games to evaluate how senders' willingness to lie to others compares to their willingness to tell hard truths, i.e., promote an outcome that the sender know is unfair to the receiver without explicitly lying. Unlike in previous work on lying when it has consequences, we do not find that antisocial behavior is less frequent when it involves lying than when it does not. In fact, we find the opposite result in the setting where there is social contact between senders and receivers, and receivers have enough information to judge whether they have been treated unfairly. In this setting, we find that senders prefer to hide behind a lie and implement the antisocial outcome by being dishonest rather than by telling the truth. These results are consistent with social image costs depending on the social proximity between senders and receivers, especially when receivers can judge the kindness of the senders' actions.

  6. H

    Replication Data for: Why Do We Lie? Distinguishing Between Competing Lying...

    • dataverse.harvard.edu
    Updated Sep 13, 2021
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    Paul Clist (2021). Replication Data for: Why Do We Lie? Distinguishing Between Competing Lying Theories [Dataset]. http://doi.org/10.7910/DVN/OXOGY3
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 13, 2021
    Dataset provided by
    Harvard Dataverse
    Authors
    Paul Clist
    License

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

    Description

    This code and dataset replicate the results in Paul Clist and Ying-yi Hong (2019) Why Do We Lie? Distinguishing Between Competing Lying Theories CBESS working paper 19-03, available at: https://ueaeco.github.io/working-papers/papers/cbess/UEA-CBESS-19-03.pdf The second experiment was preregistered: https://www.socialscienceregistry.org/trials/3547

  7. Data for www.preferencesfortruthtelling.com

    • figshare.com
    • search.datacite.org
    png
    Updated Aug 22, 2019
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    Johannes Abeler (2019). Data for www.preferencesfortruthtelling.com [Dataset]. http://doi.org/10.6084/m9.figshare.4981589.v9
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    pngAvailable download formats
    Dataset updated
    Aug 22, 2019
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Johannes Abeler
    License

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

    Description

    Data for interactive graphs on www.preferencesfortruthtelling.comThe data are based on Abeler, Nosenzo, Raymond "Preferences for truth-telling" (published in Econometrica 2019)

  8. Data from: CBS News Lying Poll, May 1997

    • icpsr.umich.edu
    ascii, delimited, sas +2
    Updated Jul 16, 2008
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    CBS News (2008). CBS News Lying Poll, May 1997 [Dataset]. http://doi.org/10.3886/ICPSR04494.v1
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    sas, spss, stata, delimited, asciiAvailable download formats
    Dataset updated
    Jul 16, 2008
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    CBS News
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/4494/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/4494/terms

    Time period covered
    May 1997
    Area covered
    United States
    Description

    This special topic poll, fielded May 6-8, 1997, is part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of other political and social issues. Respondents were asked to give their opinions of President Bill Clinton and his handling of the presidency. Views were sought on the events surrounding the 1996 Democratic fundraising activities and the White House's involvement in them, whether President Clinton and Vice President Gore did anything wrong or illegal, and whether Congress should investigate the matter. Respondents gave their opinions of Vice President Al Gore, Secretary of State Madeleine Albright, Speaker of the House Newt Gingrich, and how well members of the United States Congress were handling their jobs. Several questions asked how satisfied respondents were with their job, whether it was their dream job, and if not, what their dream job would be. Other questions addressed whether lying and keeping secrets was ever justified, how often respondents lied to others and were lied to, and their ability to tell a lie and detect when others were lying. Additional topics addressed the most important quality in a doctor, how concerned respondents were about germs, whether tobacco companies were telling the truth about the health risks of smoking, and whether they should be held legally responsible for smoking-related illness and deaths. Information was also collected on whether respondents smoked, whether they had a child in the ninth grade, and whether they identified themselves as multiracial. Demographic variables include sex, race, age, household income, education level, employment status, occupation, religious preference, frequency of religious attendance, political party affiliation, political philosophy, voter participation history and registration status, length of time living at current residence, the presence of children and teenagers in the household, and type of residential area (e.g., urban or rural).

  9. u

    Data from: Background data for: “I regret lying” VS. “I regret that I lied”:...

    • investigacion.usc.gal
    • dataverse.no
    Updated 2024
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    Romasanta, Raquel P.; Romasanta, Raquel P. (2024). Background data for: “I regret lying” VS. “I regret that I lied”: Variation in the clausal complementation profile of REGRET in American and British English [Dataset]. https://investigacion.usc.gal/documentos/67a9c7ae19544708f8c6f868
    Explore at:
    Dataset updated
    2024
    Authors
    Romasanta, Raquel P.; Romasanta, Raquel P.
    Description

    This dataset contains tabular files recording occurrences of the verb REGRET complemented by a that- or (S) -ing-complement clause (CC) in the GloWbE corpus. Tokens were retrieved using the online interface (https://www.english-corpora.org/glowbe/) and manually annotated for several syntactic and semantic variables (variety, text type, finiteness, subject in the main clause (MC), voice of the CC, meaning of the verb in the CC, subject in the CC, animacy of the subject in the CC, words in the CC, coreferentiality, intervening material, negation in the CC, temporal relation). See ReadMe file for more details. Related publication: Romasanta, Raquel P. 2022. “I regret lying” VS. “I regret that I lied”: Variation in the clausal complementation profile of REGRET in American and British English. Miscelánea 65: 37-58

  10. UK adults on lying on online dating sites and apps 2023

    • statista.com
    Updated May 22, 2024
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    Statista (2024). UK adults on lying on online dating sites and apps 2023 [Dataset]. https://www.statista.com/statistics/1468543/uk-adults-lying-dating-sites-apps/
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    Dataset updated
    May 22, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United Kingdom
    Description

    According to a survey conducted in the United Kingdom (UK) in 2023, 16 percent of online dating service users had lied about their age, while one in ten had lied about their name. Overall, eight percent had lied about their job, and four percent had lied about their current relationship status.

  11. d

    Replication Data for: Lying for Trump? Elite Cue-Taking and Expressive...

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 8, 2023
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    Shino, Enrijeta; Daniel A. Smith; Laura Uribe (2023). Replication Data for: Lying for Trump? Elite Cue-Taking and Expressive Responding on Vote Method [Dataset]. http://doi.org/10.7910/DVN/R419IQ
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    Dataset updated
    Nov 8, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Shino, Enrijeta; Daniel A. Smith; Laura Uribe
    Description

    Data and replication code for ``Lying for Trump? Elite Cue-Taking and Expressive Responding on Vote Method".

  12. Raw data: Are we modular lying cues detectors? The answer is “yes,...

    • figshare.com
    xlsx
    Updated Jan 20, 2016
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    Mathieu Arminjon (2016). Raw data: Are we modular lying cues detectors? The answer is “yes, sometimes”. [Dataset]. http://doi.org/10.6084/m9.figshare.1512355.v1
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    xlsxAvailable download formats
    Dataset updated
    Jan 20, 2016
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Mathieu Arminjon
    License

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

    Description

    LABEL EXPLANATIONS FOR STUDY 1 and 2 OF MANUSCRIPT ENTITLED "Are we modular lying cues detectors? The answer is “yes, sometimes” BY ARMINJON ET AL

  13. c

    Complicity without connection or communication, experimental data

    • datacatalogue.cessda.eu
    Updated May 27, 2025
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    Barr, A (2025). Complicity without connection or communication, experimental data [Dataset]. http://doi.org/10.5255/UKDA-SN-853002
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    Dataset updated
    May 27, 2025
    Dataset provided by
    University of Nottingham
    Authors
    Barr, A
    Time period covered
    Dec 31, 2012 - Sep 30, 2017
    Area covered
    United Kingdom
    Variables measured
    Individual
    Measurement technique
    Subjects were randomly matched into pairs. In each pair there is a Player A and a Player B. The players cannot identify or communicate with each other. Each player is asked to roll a fair six sided die once in private and report the outcome to the experimenter by writing it on a slip of paper. Each player’s report determines the monetary payoff of the other player in the pair. The experiment was conducted at the CeDEx laboratory, University of Nottingham, in May 2015. In total, 294 students, recruited through ORSEE (Greiner 2004), participated in the CG, NAc variant, and NMD treatment. Of these, 63% were females.
    Description

    We use a novel laboratory experiment involving a die rolling task embedded within a coordination game to investigate whether complicity can emerge when decision-making is simultaneous, the potential accomplices are strangers and neither communication nor signaling is possible. Then, by comparing the behavior observed in this original game to that in a variant in which die-roll reporting players are paired with passive players instead of other die-roll reporters, while everything else is held constant, we isolate the effect of having a potential accomplice on the likelihood of an individual acting immorally. We find that complicity can emerge between strangers in the absence of opportunities to communicate or signal and that having a potential accomplice increases the likelihood of an individual acting immorally.

    This network project brings together economists, psychologists, computer and complexity scientists from three leading centres for behavioural social science at Nottingham, Warwick and UEA. This group will lead a research programme with two broad objectives: to develop and test cross-disciplinary models of human behaviour and behaviour change; to draw out their implications for the formulation and evaluation of public policy. Foundational research will focus on three inter-related themes: understanding individual behaviour and behaviour change; understanding social and interactive behaviour; rethinking the foundations of policy analysis. The project will explore implications of the basic science for policy via a series of applied projects connecting naturally with the three themes. These will include: the determinants of consumer credit behaviour; the formation of social values; strategies for evaluation of policies affecting health and safety. The research will integrate theoretical perspectives from multiple disciplines and utilise a wide range of complementary methodologies including: theoretical modeling of individuals, groups and complex systems; conceptual analysis; lab and field experiments; analysis of large data sets. The Network will promote high quality cross-disciplinary research and serve as a policy forum for understanding behaviour and behaviour change.

  14. Data and Code for: Mistakes, Overconfidence and the Effect of Sharing on...

    • openicpsr.org
    Updated May 21, 2021
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    Marta Serra-Garcia; Uri Gneezy (2021). Data and Code for: Mistakes, Overconfidence and the Effect of Sharing on Detecting Lies [Dataset]. http://doi.org/10.3886/E140961V1
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    Dataset updated
    May 21, 2021
    Dataset provided by
    American Economic Associationhttp://www.aeaweb.org/
    Authors
    Marta Serra-Garcia; Uri Gneezy
    License

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

    Area covered
    United States
    Description

    Mistakes and overconfidence in detecting lies could help lies spread. Participants in our experiments observe videos in which senders either tell the truth or lie, and are incentivized to distinguish between them. We find that participants fail to detect lies, but are overconfident about their ability to do so. We use these findings to study the determinants of sharing and its effect on lie detection, finding that even when incentivized to share truthful videos, participants are more likely to share lies. Moreover, the receivers are more likely to believe shared videos. Combined, the tendency to believe lies increases with sharing.

  15. Data from: Do privacy assurances work? A study of truthfulness in healthcare...

    • zenodo.org
    • data.niaid.nih.gov
    • +1more
    csv, txt
    Updated Apr 28, 2023
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    Tamara M. Masters; Tamara M. Masters; Mark Keith; Mark Keith; Rachel Hess; Rachel Hess; Jeffrey Jenkins; Jeffrey Jenkins (2023). Do privacy assurances work? A study of truthfulness in healthcare history data collection [Dataset]. http://doi.org/10.5061/dryad.qrfj6q5k8
    Explore at:
    txt, csvAvailable download formats
    Dataset updated
    Apr 28, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Tamara M. Masters; Tamara M. Masters; Mark Keith; Mark Keith; Rachel Hess; Rachel Hess; Jeffrey Jenkins; Jeffrey Jenkins
    License

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

    Description

    Patients often provide untruthful information about their health to avoid embarrassment, evade treatment, or prevent financial loss. Privacy disclosures (e.g. HIPAA) intended to dissuade privacy concerns may actually increase patient lying. We used new mouse tracking-based technology to detect lies through mouse movement (distance and time to response) and patient answer adjustment in an online controlled study of 611 potential patients, randomly assigned to one of six treatments. Treatments differed in the notices patients received before health information was requested, including notices about privacy, benefits of truthful disclosure, and risks of inaccurate disclosure. Increased time or distance of device mouse movement and greater adjustment of answers indicate less truthfulness. Mouse tracking revealed a significant overall effect (p < 0.001) by treatment on the time to reach their final choice. The control took the least time indicating greater truthfulness and the privacy + risk group took the longest indicating the least truthfulness. Privacy, risk, and benefit disclosure statements led to greater lying. These differences were moderated by gender. Mouse tracking results largely confirmed the answer adjustment lie detection method with an overall treatment effect (p < .0001) and gender differences (p < .0001) on truthfulness. Privacy notices led to decreased patient honesty. Privacy notices should perhaps be administered well before personal health disclosure is requested to minimize patient untruthfulness. Mouse tracking and answer adjustment appear to be healthcare lie-detection methods to enhance optimal diagnosis and treatment.

  16. h

    Who never tells a lie? [Data set and Programs]

    • heidata.uni-heidelberg.de
    bin, pdf +1
    Updated Apr 5, 2017
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    Christoph Vanberg; Christoph Vanberg (2017). Who never tells a lie? [Data set and Programs] [Dataset]. http://doi.org/10.11588/DATA/10087
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    text/plain; charset=us-ascii(14678), text/plain; charset=us-ascii(17535), text/plain; charset=us-ascii(17534), text/plain; charset=us-ascii(17532), text/plain; charset=us-ascii(7842), pdf(349683), bin(30659), bin(34766), text/plain; charset=us-ascii(56927), text/plain; charset=us-ascii(66825)Available download formats
    Dataset updated
    Apr 5, 2017
    Dataset provided by
    heiDATA
    Authors
    Christoph Vanberg; Christoph Vanberg
    License

    https://heidata.uni-heidelberg.de/api/datasets/:persistentId/versions/1.1/customlicense?persistentId=doi:10.11588/DATA/10087https://heidata.uni-heidelberg.de/api/datasets/:persistentId/versions/1.1/customlicense?persistentId=doi:10.11588/DATA/10087

    Area covered
    Germany
    Description

    I experimentally investigate the hypothesis that many people avoid lying even in a situation where doing so would result in a Pareto improvement. Replicating (Erat and Gneezy, Management Science 58, 723-733, 2012), I find that a significant fraction of subjects tell the truth in a sender-receiver game where both subjects earn a higher payoff when the partner makes an incorrect guess regarding the roll of a die. However, a non-incentivized questionnaire indicates that the vast majority of these subjects expected their partner not to follow their message. I conduct two new experiments explicitly designed to test for a 'pure' aversion to lying, and find no evidence for the existence of such a motivation. I discuss the implications of the findings for moral behavior and rule following more generally.

  17. LIAR-Dataset (Valid)

    • kaggle.com
    Updated Dec 9, 2024
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    Amali ST, MSc (2024). LIAR-Dataset (Valid) [Dataset]. https://www.kaggle.com/datasets/amalistmsc/liar-dataset/data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 9, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Amali ST, MSc
    Description

    LIAR: A BENCHMARK DATASET FOR FAKE NEWS DETECTION

    William Yang Wang, "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection, to appear in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), short paper, Vancouver, BC, Canada, July 30-August 4, ACL.

    Description of the TSV format:

    Column 1: the ID of the statement ([ID].json). Column 2: the label. Column 3: the statement. Column 4: the subject(s). Column 5: the speaker. Column 6: the speaker's job title. Column 7: the state info. Column 8: the party affiliation. Column 9-13: the total credit history count, including the current statement. 9: barely true counts. 10: false counts. 11: half true counts. 12: mostly true counts. 13: pants on fire counts. Column 14: the context (venue / location of the speech or statement).

    Note that we do not provide the full-text verdict report in this current version of the dataset, but you can use the following command to access the full verdict report and links to the source documents: wget http://www.politifact.com//api/v/2/statement/[ID]/?format=json

    ======================================================================

    The original sources retain the copyright of the data.

    Note that there are absolutely no guarantees with this data, and we provide this dataset "as is", but you are welcome to report the issues of the preliminary version of this data.

    You are allowed to use this dataset for research purposes only.

    For more question about the dataset, please contact: William Wang, william@cs.ucsb.edu

    v1.0 04/23/2017

  18. d

    Replication Data for: \"Seeing lies and laying blame: Partisanship and US...

    • search.dataone.org
    Updated Mar 6, 2024
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    Kaitlin Peach; Joseph Ripberger; Kuhika Gupta; Andrew Fox; Hank Jenkins-Smith; Carol Silva (2024). Replication Data for: \"Seeing lies and laying blame: Partisanship and US public perceptions about disinformation\" [Dataset]. http://doi.org/10.7910/DVN/PRP4WX
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    Dataset updated
    Mar 6, 2024
    Dataset provided by
    Harvard Dataverse
    Authors
    Kaitlin Peach; Joseph Ripberger; Kuhika Gupta; Andrew Fox; Hank Jenkins-Smith; Carol Silva
    Description

    These are the replication materials for "Seeing lies and laying blame: Partisanship and US public perceptions about disinformation" by Kaitlin Peach, Joseph Ripberger, Kuhika Gupta, Andrew Fox, Hank Jenkins-Smith, and Carol Silva. The data comes from the 2021 National Security Survey (NS21) administered by the Institute for Public Policy Research and Analysis (IPPRA) at the University of Oklahoma. The survey was fielded in December 2021, using an online questionnaire of 2,036 US adults (18+). The sample matches the characteristics of the U.S. population. This dataset only contains questions used in the study. A document detailing the survey questions with variable names, the R script, and CSV dataset are included. Funding for data collection was provided by the Office of the Vice President for Research and Partnerships at the University of Oklahoma.

  19. f

    Descriptive Statistics for Lying Subscales.

    • plos.figshare.com
    xls
    Updated Jun 9, 2023
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    Heather Mann; Ximena Garcia-Rada; Daniel Houser; Dan Ariely (2023). Descriptive Statistics for Lying Subscales. [Dataset]. http://doi.org/10.1371/journal.pone.0109591.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 9, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Heather Mann; Ximena Garcia-Rada; Daniel Houser; Dan Ariely
    License

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

    Description

    Note. Internal consistency (α), mean (), and standard deviation (s) statistics are presented for each of the four lying subscales, for P1 and P2 participant samples.Descriptive Statistics for Lying Subscales.

  20. n

    Data from: Lying in a 3T MRI scanner induces neglect-like spatial attention...

    • data.niaid.nih.gov
    • search.dataone.org
    • +2more
    zip
    Updated Oct 5, 2021
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    Axel Lindner; Daniel Wiesen; Hans-Otto Karnath (2021). Lying in a 3T MRI scanner induces neglect-like spatial attention bias [Dataset]. http://doi.org/10.5061/dryad.6t1g1jx05
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    zipAvailable download formats
    Dataset updated
    Oct 5, 2021
    Dataset provided by
    Hertie Institute for Clinical Brain Research
    Authors
    Axel Lindner; Daniel Wiesen; Hans-Otto Karnath
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Description

    Exposing subjects to the magnetic field of a 3T MRI scanner stimulates the vestibular organ and thereby induces - besides a VOR - horizontal biases in visual search and in subjective straight ahead, which are similar to those seen in stroke patients with spatial neglect.

    Methods The dataset contains eye-tracking data. Please also refer to related Matlab-files on Zenodo, which document both how our eye-data were originally generated and allow to analyze these data. Please refer to our original publication (Lindner et al. eLife 2021;10:e71076. DOI: https://doi.org/10.7554/eLife.71076) as well as to the included ReadMe.txt-file for details.

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Statista (2024). U.S. online dating service users lying on their profiles 2024 [Dataset]. https://www.statista.com/statistics/1481187/us-online-dating-users-lying-on-profiles/
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U.S. online dating service users lying on their profiles 2024

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Dataset updated
Jul 22, 2024
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
Mar 27, 2024 - Apr 1, 2024
Area covered
United States
Description

According to a survey conducted in April 2024 in the United States, one in five online dating service users had lied about their age on their dating profile, while 14 percent had lied about their income. A further 14 percent had lied about their hobbies and interests, and 12 percent had lied about their height.

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