Thursday, 07/25/2024 to 07/25/2024, 3:15 pm to 4:00 pm. ARCHIVED EVENT
Location: Online
Social, Behavioral, & Economic COVID Coordinating Center (SBE CCC) Webinar
Join the Zoom meeting here.
Abstract:
Among the numerous explanations that have been offered for recent errors in survey estimates of COVID-19 vaccine uptake rates and pre-election polling, non-ignorable selection bias, where the probability of responding to a survey is a function of the measure of interest (even after conditioning on other relevant covariates often used for weighting adjustments), has received relatively less focus in the academic literature. Under this type of selection mechanism, estimates of proportions may be subject to significant bias, even after standard weighting adjustments. Until recently, methods for measuring and adjusting for this type of non-ignorable selection bias have been unavailable. Fortunately, recent developments in the methodological literature have provided researchers with easy-to-use measures of non-ignorable selection bias. In this study, we apply a new measure that has been developed specifically for estimated proportions to this challenging problem. We analyze data from the U.S. Census Household Pulse Survey and 18 different pre-election polls (nine different telephone polls conducted in eight different states prior to the U.S. Presidential election in 2020, and nine different pre-election polls conducted either online or via telephone in Great Britain prior to the 2015 General Election). We rigorously evaluate the ability of this new measure to detect and adjust for selection bias in estimates of the proportion of individuals receiving COVID-19 vaccinations and the proportion of likely voters that will vote for a specific candidate, using official outcomes from the CDC and each election as benchmarks and alternative data sources for estimating key characteristics of the populations of interest in each context.
Speaker bio:
Brady T. West is a Research Professor in the Survey Methodology Program, located within the Survey Research Center at the Institute for Social Research on the University of Michigan-Ann Arbor (U-M) campus. He earned his PhD from the Michigan Program in Survey and Data Science in 2011. Before that, he received an MA in Applied Statistics from the U-M Statistics Department in 2002, being recognized as an Outstanding First-year Applied Masters student, and a BS in Statistics with Highest Honors and Highest Distinction from the U-M Statistics Department in 2001. His current research interests include the implications of measurement error in auxiliary variables and survey paradata for survey estimation, selection bias in surveys, responsive/adaptive survey design, interviewer effects, and multilevel regression models for clustered and longitudinal data.