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Reviews of "Are We There Yet? Big Data Significantly Overestimates COVID-19 Vaccination in the US"

Reviewers: Y Xie (Michigan State University) | 📗📗📗📗◻️

Published onApr 14, 2022
Reviews of "Are We There Yet? Big Data Significantly Overestimates COVID-19 Vaccination in the US"
key-enterThis Pub is a Review of
Unrepresentative Big Surveys Significantly Overestimate US Vaccine Uptake
Description

AbstractSurveys are a crucial tool for understanding public opinion and behavior, 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 impact 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: Delphi-Facebook 2,3 (about 250,000 responses per week) and Census Household Pulse 4 (about 75,000 per week). By May 2021, Delphi-Facebook overestimated uptake by 17 percentage points and Census Household Pulse by 14, compared to a benchmark from the Centers for Disease Control and Prevention (CDC). Moreover, their large data sizes led to minuscule margins of error on the incorrect estimates. In contrast, an Axios-Ipsos online panel 5 with about 1,000 responses following survey research best practices 6 provided reliable estimates and uncertainty. We decompose observed error using a recent analytic framework 1 to explain the inaccuracy in the three surveys. We then analyze 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 far more than data quantity, and compensating the former with the latter is a mathematically provable losing proposition.

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Reviewer 1 (Yuying X…) | 📗📗📗📗◻️

RR:C19 Strength of Evidence Scale Key

📕 ◻️◻️◻️◻️ = Misleading

📙📙 ◻️◻️◻️ = Not Informative

📒📒📒 ◻️◻️ = Potentially Informative

📗📗📗📗◻️ = Reliable

📘📘📘📘📘 = Strong

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