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

Abstract Number:

2099 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Youqi Yang (1), Walter Dempsey (1), Peisong Han (2), Yashwant Deshmukh (3), Sylvia Richardson (4), Brian Tom (4), Bhramar Mukherjee (1)

Institutions:

(1) University of Michigan, N/A, (2) Gilead Sciences, N/A, (3) CVoter Foundation, N/A, (4) University of Cambridge, N/A

Co-Author(s):

Walter Dempsey  
University of Michigan
Peisong Han  
Gilead Sciences
Yashwant Deshmukh  
CVoter Foundation
Sylvia Richardson  
University of Cambridge
Brian Tom  
University of Cambridge
Bhramar Mukherjee  
University of Michigan

First Author:

Youqi Yang  
University of Michigan

Presenting Author:

Youqi Yang  
N/A

Abstract Text:

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.

Keywords:

Big Data Paradox|Non-probability sample|Selection bias|Online survey|Vaccine uptake|

Sponsors:

Survey Research Methods Section

Tracks:

Non-probability Samples

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