Combining Data from Probability and Nonprobability Samples Using Doubly Robust Methods

• Activity Number: 544 - Assessing Different Approaches to Combining Probability and Nonprobability Samples via Simulations
• Type: Topic Contributed
• Date/Time: Thursday, August 11, 2022 : 10:30 AM to 12:20 PM
• Sponsor: Social Statistics Section
• Author(s): Michael Elliott* and Ali Rafei and Carol Flannagan
• Companies: University of Michigan and Meta and University of Michigan
• Keywords: Non-probability sample; double robustness; augmented inverse propensity weighting

Abstract

Population inference using combinations of probability and non-probability samples is an increasingly critical area of research. A variety of “doubly-robust” methods that combine model-based prediction estimators with estimators weighted to account for selection bias have been developed. Here we propose a Bayesian approach that uses a prediction model with a flexible function of the estimated propensity scores as a predictor to impute the outcome for either non-sampled units of the population or units of the reference survey. We will use this method in a simulation study common to the other papers presented in this session. This study will use two sampling frames, one a subset of the other, using survey completes from a study of Arts and Culture during COVID-19. Frame 1 will be the full population frame, consisting of all survey completes, from which probability samples will be selected. Frame 2 will be a nonrandom subset of Frame 1 with known coverage biases from which non-probability samples will be selected. Assessments of the proposed method will be undertaken via estimates of bias, interval coverage, and mean square error.