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Activity Number: 442 - State-Of-The-Art Inferential Approaches for Non-Probability Samples
Type: Invited
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Survey Research Methods Section
Abstract #300633
Title: Decomposing Selection Bias in Nonprobability Surveys
Author(s): Andrew Mercer*
Companies: Pew Research Center

To date, most research into the sources of selection bias in estimates from nonprobability survey samples has adopted the Total Survey Error (TSE) paradigm’s designation of selection bias as attributable to undercoverage or nonresponse. This error typology has proven less helpful for nonprobability surveys because it implicitly assumes that inferences depend on randomization. This paper proposes an alternative typology selection bias for nonprobability surveys based on principles borrowed from the field of causal inference. It describes selection bias in terms of the three conditions that are required for a statistical model to correct systematic differences between a sample and the target population: exchangeability, common support, and composition. We show how selection bias can be decomposed into separate additive components associated with each condition. Using 10 parallel nonprobability surveys conducted by Pew Research Center, we estimate these components for six measures of civic engagement using the 2013 Current Population Survey Civic Engagement Supplement as a reference sample. We find that a large majority of the bias can be attributed to a lack of exchangeability.

Authors who are presenting talks have a * after their name.

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