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Unrepresentative big surveys significantly overestimated US vaccine uptake
by
Meng, Xiao-Li
, Flaxman, Seth
, Bradley, Valerie C.
, Isakov, Michael
, Sejdinovic, Dino
, Kuriwaki, Shiro
in
639/705/531
/ 692/699/255
/ 706/648/697
/ Benchmarking
/ Benchmarks
/ Bias
/ Big Data
/ Census
/ Centers for Disease Control and Prevention, U.S
/ Confidence intervals
/ Control
/ Coronaviruses
/ COVID-19 - epidemiology
/ COVID-19 - prevention & control
/ COVID-19 vaccines
/ COVID-19 Vaccines - administration & dosage
/ Datasets as Topic - standards
/ Disease control
/ Epidemics
/ Estimates
/ Female
/ Forecasts and trends
/ Health behavior
/ Health Care Surveys - standards
/ Humanities and Social Sciences
/ Humans
/ Male
/ multidisciplinary
/ Polls & surveys
/ Public opinion
/ Research Design
/ Sample Size
/ Science
/ Science (multidisciplinary)
/ Social Media
/ Statistical analysis
/ United Kingdom
/ United States
/ United States - epidemiology
/ Vaccination - statistics & numerical data
/ Vaccination Hesitancy - statistics & numerical data
/ Vaccine hesitancy
/ Vaccines
2021
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Unrepresentative big surveys significantly overestimated US vaccine uptake
by
Meng, Xiao-Li
, Flaxman, Seth
, Bradley, Valerie C.
, Isakov, Michael
, Sejdinovic, Dino
, Kuriwaki, Shiro
in
639/705/531
/ 692/699/255
/ 706/648/697
/ Benchmarking
/ Benchmarks
/ Bias
/ Big Data
/ Census
/ Centers for Disease Control and Prevention, U.S
/ Confidence intervals
/ Control
/ Coronaviruses
/ COVID-19 - epidemiology
/ COVID-19 - prevention & control
/ COVID-19 vaccines
/ COVID-19 Vaccines - administration & dosage
/ Datasets as Topic - standards
/ Disease control
/ Epidemics
/ Estimates
/ Female
/ Forecasts and trends
/ Health behavior
/ Health Care Surveys - standards
/ Humanities and Social Sciences
/ Humans
/ Male
/ multidisciplinary
/ Polls & surveys
/ Public opinion
/ Research Design
/ Sample Size
/ Science
/ Science (multidisciplinary)
/ Social Media
/ Statistical analysis
/ United Kingdom
/ United States
/ United States - epidemiology
/ Vaccination - statistics & numerical data
/ Vaccination Hesitancy - statistics & numerical data
/ Vaccine hesitancy
/ Vaccines
2021
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Unrepresentative big surveys significantly overestimated US vaccine uptake
by
Meng, Xiao-Li
, Flaxman, Seth
, Bradley, Valerie C.
, Isakov, Michael
, Sejdinovic, Dino
, Kuriwaki, Shiro
in
639/705/531
/ 692/699/255
/ 706/648/697
/ Benchmarking
/ Benchmarks
/ Bias
/ Big Data
/ Census
/ Centers for Disease Control and Prevention, U.S
/ Confidence intervals
/ Control
/ Coronaviruses
/ COVID-19 - epidemiology
/ COVID-19 - prevention & control
/ COVID-19 vaccines
/ COVID-19 Vaccines - administration & dosage
/ Datasets as Topic - standards
/ Disease control
/ Epidemics
/ Estimates
/ Female
/ Forecasts and trends
/ Health behavior
/ Health Care Surveys - standards
/ Humanities and Social Sciences
/ Humans
/ Male
/ multidisciplinary
/ Polls & surveys
/ Public opinion
/ Research Design
/ Sample Size
/ Science
/ Science (multidisciplinary)
/ Social Media
/ Statistical analysis
/ United Kingdom
/ United States
/ United States - epidemiology
/ Vaccination - statistics & numerical data
/ Vaccination Hesitancy - statistics & numerical data
/ Vaccine hesitancy
/ Vaccines
2021
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Unrepresentative big surveys significantly overestimated US vaccine uptake
Journal Article
Unrepresentative big surveys significantly overestimated US vaccine uptake
2021
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Overview
Surveys are a crucial tool for understanding public opinion and behaviour, and their accuracy depends on maintaining statistical representativeness of their target populations by minimizing biases from all sources. Increasing data size shrinks confidence intervals but magnifies the effect of survey bias: an instance of the Big Data Paradox
1
. Here we demonstrate this paradox in estimates of first-dose COVID-19 vaccine uptake in US adults from 9 January to 19 May 2021 from two large surveys: Delphi–Facebook
2
,
3
(about 250,000 responses per week) and Census Household Pulse
4
(about 75,000 every two weeks). In May 2021, Delphi–Facebook overestimated uptake by 17 percentage points (14–20 percentage points with 5% benchmark imprecision) and Census Household Pulse by 14 (11–17 percentage points with 5% benchmark imprecision), compared to a retroactively updated benchmark the Centers for Disease Control and Prevention published on 26 May 2021. Moreover, their large sample sizes led to miniscule margins of error on the incorrect estimates. By contrast, an Axios–Ipsos online panel
5
with about 1,000 responses per week following survey research best practices
6
provided reliable estimates and uncertainty quantification. We decompose observed error using a recent analytic framework
1
to explain the inaccuracy in the three surveys. We then analyse the implications for vaccine hesitancy and willingness. We show how a survey of 250,000 respondents can produce an estimate of the population mean that is no more accurate than an estimate from a simple random sample of size 10. Our central message is that data quality matters more than data quantity, and that compensating the former with the latter is a mathematically provable losing proposition.
An analysis of three surveys of COVID-19 vaccine behaviour shows that larger surveys overconfidently overestimated vaccine uptake, a demonstration of how larger sample sizes can paradoxically lead to less accurate estimates.
Publisher
Nature Publishing Group UK,Nature Publishing Group
Subject
/ Bias
/ Big Data
/ Census
/ Centers for Disease Control and Prevention, U.S
/ Control
/ COVID-19 - prevention & control
/ COVID-19 Vaccines - administration & dosage
/ Datasets as Topic - standards
/ Female
/ Health Care Surveys - standards
/ Humanities and Social Sciences
/ Humans
/ Male
/ Science
/ United States - epidemiology
/ Vaccination - statistics & numerical data
/ Vaccination Hesitancy - statistics & numerical data
/ Vaccines
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