Exploring the big data paradox for various estimands using vaccination data from a global survey

Oregon Convention Center 

Selection bias poses a substantial challenge to valid statistical inference in non-probability samples. This study compared estimates of the first-dose COVID-19 vaccination rates among Indian adults in 2021 from a large non-probability 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 (CoWIN). Notably, CTIS exhibits a larger estimation error on average (0.37) compared to CVoter (0.14). Additionally, we explored the accuracy of CTIS in estimating successive differences (over time) and subgroup differences in mean vaccine uptakes (for females versus males). 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 US and may not apply equally to every estimand of interest.

Big Data Paradox

Non-probability sample

Selection bias

Online survey

Vaccine uptake 

Survey Research Methods Section