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Selection bias poses a challenge to statistical inference validity innon-probability surveys. This study compared estimates of the first-doseCOVID-19 vaccination rates among Indian adults in 2021 from a largenon-probability survey, COVID-19 Trends and Impact Survey (CTIS), and a smallprobability survey, the Center for Voting Options and Trends in ElectionResearch (CVoter), against benchmark data from the COVID Vaccine IntelligenceNetwork (CoWIN). Notably, CTIS exhibits a larger estimation error (0.39)compared to CVoter (0.16). Additionally, we investigated the estimationaccuracy of the CTIS when using a relative scale and found a significantincrease in the effective sample size by altering the estimand from the overallvaccination rate. These results suggest that the big data paradox can manifestin countries beyond the US and it may not apply to every estimand of interest.

Exploring the big data paradox for various estimands using vaccination data from the global COVID-19 Trends and Impact Survey (CTIS)