How well do ordinary Americans forecast the growth of COVID-19?
Across three experiments (N = 1565), we investigated how forecasts about the spread of COVID 19 are impacted by data trends, and whether patterns of misestimation predict adherence to social-distancing guidelines. We also investigated how mode of data presentation influences forecasting of future ca...
| Publicado en: | Memory & Cognition Vol. 50; no. 7; pp. 1363 - 1381 |
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| Autores principales: | , , , , , , , |
| Formato: | Artículo |
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Springer Nature
Oct2022
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=159303717&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 159303717 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0090502X MEG jtl: Memory & Cognition issn: 0090502X maglogo: N pubinfo: dt: Oct2022 vid: 50 iid: 7 pid: 237 pub: Springer Nature artinfo: ui: 159303717 10.3758/s13421-022-01288-0 ppf: 1363 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.3MB tig: atl: How well do ordinary Americans forecast the growth of COVID-19? aug: au: Fansher, Madison Adkins, Tyler J. Lewis, Richard L. Boduroglu, Aysecan Lalwani, Poortata Quirk, Madelyn Shah, Priti Jonides, John affil: Department of Psychology, University of Michigan, Ann Arbor, MI, USA Department of Linguistics, University of Michigan, Ann Arbor, MI, USA Weinberg Institute for Cognitive Science, University of Michigan, Ann Arbor, MI, USA Department of Psychology, Bogazici University, Istanbul, Turkey su: United States Confidence Forecasting Public opinion COVID-19 Information display systems Social distancing Sensitivity & specificity (Statistics) sug: subj: Confidence Forecasting Public opinion United States COVID-19 Information display systems Social distancing Sensitivity & specificity (Statistics) keyword: Data visualization Data visualization ab: Across three experiments (N = 1565), we investigated how forecasts about the spread of COVID 19 are impacted by data trends, and whether patterns of misestimation predict adherence to social-distancing guidelines. We also investigated how mode of data presentation influences forecasting of future cases by showing participants data on the number of COVID-19 cases from a 5-week period in either graphical, tabular, or text-only form. We consistently found that people shown tables produced more accurate forecasts compared to people shown line-graphs of the same data; yet people shown line-graphs were more confident in their estimates. These findings suggest that graphs engender false-confidence in the accuracy of forecasts, that people's forecasts of future cases have important implications for their attitudes concerning social distancing, and that tables may be better than graphs for informing the public about the trajectory of COVID-19. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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