B0604
Title: Integrating data from multiple surveys to improve estimation
Authors: Joseph Sakshaug - LMU-Munich and Institute for Employment Research (Germany) [presenting]
Camilla Salvatore - Utrecht University (Netherlands)
Arkadiusz Wisniowski - University of Manchester (United Kingdom)
Bella Struminskaya - Utrecht University (Netherlands)
Silvia Biffignandi - University of Bergamo (Italy)
Abstract: Probability sample surveys are considered the gold standard for population-based inference but face many challenges due to decreasing response rates, relatively small sample sizes, and increasing costs. In contrast, the use of non-probability sample surveys has increased significantly due to their convenience, large sample sizes, and relatively low costs, but they are susceptible to large selection biases and unknown selection mechanisms. Integrating both sample types in a way that exploits their strengths and overcomes their weaknesses is an ongoing area of methodological research. A method of supplementing probability samples with non-probability samples is proposed to improve analytic inference for logistic regression coefficients and potentially reduce survey costs. Specifically, a Bayesian framework is considered, where inference is based on a probability survey with small sample size and supplementary auxiliary information from a less-expensive (but potentially biased) non-probability sample survey fielded in parallel and is provided naturally through the prior structure. The performance of several strongly informative priors constructed from the non-probability sample information is evaluated through a simulation study and real-data application. Overall, the proposed priors reduce the mean-squared error (MSE) of regression coefficients or, in the worst case, perform similarly to a weakly informative (baseline) prior that doesn't utilize any non-probability information.
