On extreme perception bias
This preliminary note investigates perception bias: To what extent do individual opinions confound reality? We estimate the relative gap between self-declared estimates and real data. We asked a sample of Philippine respondents about the incidence of diabetes and smartphone usage in their country. W...
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oai:animorepository.dlsu.edu.ph:faculty_research-91002023-04-17T01:27:36Z On extreme perception bias Molina, Imelda Revilla Aguilar, Emerico H. Puzon, Klarizze Martin This preliminary note investigates perception bias: To what extent do individual opinions confound reality? We estimate the relative gap between self-declared estimates and real data. We asked a sample of Philippine respondents about the incidence of diabetes and smartphone usage in their country. We observed a trend of judgement miscalibration. Responses exhibit significant deviation from facts, e.g. inaccuracies can go as high as seven times the real value. Especially for estimates on smartphone ownership, bootstrapped quantile regression models show that perception bias is associated with age. 2020-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/9101 Faculty Research Work Animo Repository Public opinion Judgment (Logic) Quantile regression Computer Sciences |
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De La Salle University |
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De La Salle University Library |
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Asia |
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Philippines Philippines |
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De La Salle University Library |
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Public opinion Judgment (Logic) Quantile regression Computer Sciences |
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Public opinion Judgment (Logic) Quantile regression Computer Sciences Molina, Imelda Revilla Aguilar, Emerico H. Puzon, Klarizze Martin On extreme perception bias |
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This preliminary note investigates perception bias: To what extent do individual opinions confound reality? We estimate the relative gap between self-declared estimates and real data. We asked a sample of Philippine respondents about the incidence of diabetes and smartphone usage in their country. We observed a trend of judgement miscalibration. Responses exhibit significant deviation from facts, e.g. inaccuracies can go as high as seven times the real value. Especially for estimates on smartphone ownership, bootstrapped quantile regression models show that perception bias is associated with age. |
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text |
| author |
Molina, Imelda Revilla Aguilar, Emerico H. Puzon, Klarizze Martin |
| author_facet |
Molina, Imelda Revilla Aguilar, Emerico H. Puzon, Klarizze Martin |
| author_sort |
Molina, Imelda Revilla |
| title |
On extreme perception bias |
| title_short |
On extreme perception bias |
| title_full |
On extreme perception bias |
| title_fullStr |
On extreme perception bias |
| title_full_unstemmed |
On extreme perception bias |
| title_sort |
on extreme perception bias |
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Animo Repository |
| publishDate |
2020 |
| url |
https://animorepository.dlsu.edu.ph/faculty_research/9101 |
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