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Exploring the Big Data Paradox for various estimands using vaccination data from the global COVID-19 Trends and Impact Survey (CTIS).
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- Author(s): Yang Y;Yang Y; Dempsey W; Dempsey W; Han P; Han P; Deshmukh Y; Deshmukh Y; Richardson S; Richardson S; Tom B; Tom B; Mukherjee B; Mukherjee B
- Source:
Science advances [Sci Adv] 2024 May 31; Vol. 10 (22), pp. eadj0266. Date of Electronic Publication: 2024 May 31.- Publication Type:
Journal Article- Language:
English - Source:
- Additional Information
- Source: Publisher: American Association for the Advancement of Science Country of Publication: United States NLM ID: 101653440 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2375-2548 (Electronic) Linking ISSN: 23752548 NLM ISO Abbreviation: Sci Adv Subsets: MEDLINE
- Publication Information: Original Publication: Washington, DC : American Association for the Advancement of Science, [2015]-
- Subject Terms:
- Abstract: Selection bias poses a substantial challenge to valid statistical inference in nonprobability samples. This study compared estimates of the first-dose COVID-19 vaccination rates among Indian adults in 2021 from a large nonprobability sample, the COVID-19 Trends and Impact Survey (CTIS), and a small probability survey, the Center for Voting Options and Trends in Election Research (CVoter), against national benchmark data from the COVID Vaccine Intelligence Network. Notably, CTIS exhibits a larger estimation error on average (0.37) compared to CVoter (0.14). Additionally, we explored the accuracy (regarding mean squared error) of CTIS in estimating successive differences (over time) and subgroup differences (for females versus males) in mean vaccine uptakes. Compared to the overall vaccination rates, targeting these alternative estimands comparing differences or relative differences in two means increased the effective sample size. These results suggest that the Big Data Paradox can manifest in countries beyond the United States and may not apply equally to every estimand of interest.
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- Publication Date: Date Created: 20240531 Date Completed: 20240531 Latest Revision: 20240811
- Publication Date: 20240812
- Accession Number: PMC11314312
- Accession Number: 10.1126/sciadv.adj0266
- Accession Number: 38820165
- Source:
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