Morgan S. Earp
Morgan Earp, Melissa Mitchell, Jaki McCarthy and Frauke Kreuter
Increasing nonresponse rates in federal surveys and potentially biased survey estimates are a growing concern, especially with regard to establishment surveys. Unlike household surveys, not all establishments contribute equally to survey estimates. With regard to agricultural surveys, if an extremely large farm fails to complete a survey, the United States Department of Agriculture (USDA) could potentially underestimate average acres operated among other things. In order to identify likely nonrespondents prior to data collection, the USDA’s National Agricultural Statistics Service (NASS) began modeling nonresponse using Census of Agriculture data and prior Agricultural Resource Management Survey (ARMS) response history. Using an ensemble of classification trees, NASS has estimated nonresponse propensities for ARMS that can be used to predict nonresponse and are correlated with key ARMS estimates.
Morgan Earp, Daniell Toth, Polly Phipps and Charlotte Oslund
This article introduces and discusses a method for conducting an analysis of nonresponse for a longitudinal establishment survey using regression trees. The methodology consists of three parts: analysis during the frame refinement and enrollment phases, common in longitudinal surveys; analysis of the effect of time on response rates during data collection; and analysis of the potential for nonresponse bias. For all three analyses, regression tree models are used to identify establishment characteristics and subgroups of establishments that represent vulnerabilities during the data collection process. This information could be used to direct additional resources to collecting data from identified establishments in order to improve the response rate.