Upcoming Webinar Featuring SBE CCC Co-Investigator Brady West

Save the date for an upcoming SBE CCC webinar featuring Brady West

When: October 23, 2025 from 3 – 4 pm EST

Location: On Zoom (no registration required)

Title: Improved Mass Imputation in Probability Samples via Adjustment of Imputation Models Based on Non-Probability Samples for Selection Bias

Description: Methods for integrating probability and non-probability samples have grown in popularity in recent years, in part because it may be infeasible to measure a variable of scientific interest in a large probability sample, but feasible to do so in a non-probability sample. One such method is mass imputation, where a variable that is not measured in the probability sample is fully imputed in that sample using a model fitted to the non-probability sample. A fundamental assumption of this method is that the imputation model fitted to the non-probability sample is transportable to the probability sample, meaning that the same regression function for the variable of interest holds for both samples. If the selection mechanism for the non-probability sample is non-ignorable, given the variable of interest, then there may be significant bias in the imputation model coefficients, resulting in poor imputations and subsequent bias in the mass imputation estimator. This paper leverages recent work on novel measures of selection bias for regression coefficients to propose an adjustment of the imputation model coefficients for selection bias prior to imputation. Via two simulation studies and a case study, we demonstrate the ability of this method to improve estimates produced using mass imputation procedures.

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 and was elected as a Fellow of the American Statistical Association in 2022. He conducts research in total survey error and survey estimation more generally, and his current research interests include selection bias in surveys, responsive and adaptive survey designs, interviewer effects, survey paradata, and multilevel regression models for clustered and longitudinal data.