<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></abstract>

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.

Modeling Nonresponse in Establishment Surveys: Using an Ensemble Tree Model to Create Nonresponse Propensity Scores and Detect Potential Bias in an Agricultural Survey

Bureau of Labor Statistics – Office of Survey Methods Research, PSB Suite 1950, 2 Massachusetts Avenue, NE Washington District of Columbia 20212, U.S.A.

USDA – National Agricultural Statistics Service, Fairfax, Virginia, U.S.A.

University of Maryland – JPSM, 1218 Lefrak Hall, College Park, MD 20742, Maryland 20742, U.S.A.

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Increasing nonresponse rates in federal surveys and potentially biased survey estimates are a growing concern, especially with regard to establishment surveys....

Modeling Nonresponse in Establishment Surveys: Using an Ensemble Tree Model to Create Nonresponse Propensity Scores and Detect Potential Bias in an Agricultural Survey

Data publikacji: 11 gru 2014

Zakres stron: 701 - 719

Otrzymano: 01 gru 2012

Przyjęty: 01 wrz 2014

DOI: https://doi.org/10.2478/jos-2014-0044

Słowa kluczowe
Nonresponse bias, propensity scores, classification trees, ensemble trees

© by Morgan Earp

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Modeling Nonresponse in Establishment Surveys: Using an Ensemble Tree Model to Create Nonresponse Propensity Scores and Detect Potential Bias in an Agricultural Survey

Data publikacji: 11 gru 2014

Zakres stron: 701 - 719

Otrzymano: 01 gru 2012

Przyjęty: 01 wrz 2014

DOI: https://doi.org/10.2478/jos-2014-0044

Słowa kluczoweNonresponse bias, propensity scores, classification trees, ensemble trees

© by Morgan Earp

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Nonresponse bias, propensity scores, classification trees, ensemble trees