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Activity Number: 119 - Statistical Data Editing Modernisation
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Government Statistics Section
Abstract #305017
Title: Evaluating Imputation Methods for the Agricultural Resource Management Survey
Author(s): Darcy Miller* and Andrew Dau and Audra Zakzeski
Companies: National Agricultural Statistics Service and National Agricultural Statistics Service and National Agricultural Statistics Service
Keywords: Item Nonresponse; Imputation; Agriculture; Missing Values

The National Agricultural Statistics Service (NASS), in conjunction with the Economic Research Service (ERS), conducts the three-phase Agricultural Resource Management Survey (ARMS) to study the economic well-being of farm households. Since 2015, Iterative Sequential Regression (ISR), a multivariate imputation methodology, has been used to address item nonresponse in the third phase of the survey (ARMS 3). ISR is an in-house developed software program that requires a significant amount of support to maintain. Also, ISR was developed for use on continuous and semi-continuous data, and NASS wants to impute other data types, including categorical and ordinal data. Hence, NASS is exploring alternative “off-the-shelf” imputation approaches, specifically, IVEware, a product of the University of Michigan, and the Fully Conditional Specification Option in SAS® PROC MI. A 2018 JSM paper empirically compared ISR to these two alternatives using a subset of ARMS 3 data. This paper builds on that simulation work and culminates in an impact assessment of a change to one of the alternatives on reported estimates and operational resources through an application to the full ARMS 3 dataset.

Authors who are presenting talks have a * after their name.

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