A bridging model to reconcile statistics based on data from multiple surveys

Abstract

Surveys designed to collect data on similar variables using samples representing the same population may still result in different estimates due, for example, to differences in sample designs or modes of data collection. Considered in this paper is the case where two surveys were conducted concurrently, with one using the same methodology as used in prior rounds of the survey and the other using an updated methodology, resulting in substantial differences in several key estimates. Due to differences in sample size, only the latter survey was detailed enough for disaggregated-level estimates of publishable quality. We propose a hierarchical model to account for discrepancies in the estimates from the two surveys and a Bayesian approach for producing reliable estimates at various levels of aggregation. The model relies on a common latent structure at the disaggregated level to allow “bridging” between the two surveys. The methodology is applied to the 2016 National Survey of Fishing, Hunting and Wildlife-Associated Recreation and the 2016 50-State Surveys of Fishing, Hunting and Wildlife-Related Recreation. Aligning these two surveys is critical to extend the series of related statistics that have been published since 1955, allowing for meaningful comparisons over time despite the change in survey methodology.