The Impact of Targeted Data Collection on Nonresponse Bias in an Establishment Survey: A Simulation Study of Adaptive Survey Design
Nonresponse rates have been growing over time leading to concerns about survey data quality. Adaptive designs seek to allocate scarce resources by targeting specific subsets of sampled units for additional effort or a different recruitment protocol. In order to be effective in reducing nonresponse,...
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doaj-278ba2053f9f4044b9c65f074683cfcb2021-09-06T19:40:52ZengSciendoJournal of Official Statistics2001-73672017-09-0133385787110.1515/jos-2017-0039jos-2017-0039The Impact of Targeted Data Collection on Nonresponse Bias in an Establishment Survey: A Simulation Study of Adaptive Survey DesignMcCarthy Jaki0Wagner James1Sanders Herschel Lisette2USDA, National Agricultural Statistics Service, 3251 Old Lee Highway, Fairfax, VA 22030, United States of America.University of Michigan, Institute for Social Research, 426 Thompson St. Room 4050, Ann Arbor, MI 48104, United States of America.RTI International, 701 13th St NW, Suite 750, Washington, DC 20005, United States of America.Nonresponse rates have been growing over time leading to concerns about survey data quality. Adaptive designs seek to allocate scarce resources by targeting specific subsets of sampled units for additional effort or a different recruitment protocol. In order to be effective in reducing nonresponse, the identified subsets of the sample need two key features: 1) their probabilities of response can be impacted by changing design features, and 2) once they have responded, this can have an impact on estimates after adjustment. The National Agricultural Statistics Service (NASS) is investigating the use of adaptive design techniques in the Crops Acreage, Production, and Stocks Survey (Crops APS). The Crops APS is a survey of establishments which vary in size and, hence, in their potential impact on estimates. In order to identify subgroups for targeted designs, we conducted a simulation study that used Census of Agriculture (COA) data as proxies for similar survey items. Different patterns of nonresponse were simulated to identify subgroups that may reduce estimated nonresponse bias when their response propensities are changed.https://doi.org/10.1515/jos-2017-0039adaptive survey designnonresponse biasestablishment surveys |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
McCarthy Jaki Wagner James Sanders Herschel Lisette |
spellingShingle |
McCarthy Jaki Wagner James Sanders Herschel Lisette The Impact of Targeted Data Collection on Nonresponse Bias in an Establishment Survey: A Simulation Study of Adaptive Survey Design Journal of Official Statistics adaptive survey design nonresponse bias establishment surveys |
author_facet |
McCarthy Jaki Wagner James Sanders Herschel Lisette |
author_sort |
McCarthy Jaki |
title |
The Impact of Targeted Data Collection on Nonresponse Bias in an Establishment Survey: A Simulation Study of Adaptive Survey Design |
title_short |
The Impact of Targeted Data Collection on Nonresponse Bias in an Establishment Survey: A Simulation Study of Adaptive Survey Design |
title_full |
The Impact of Targeted Data Collection on Nonresponse Bias in an Establishment Survey: A Simulation Study of Adaptive Survey Design |
title_fullStr |
The Impact of Targeted Data Collection on Nonresponse Bias in an Establishment Survey: A Simulation Study of Adaptive Survey Design |
title_full_unstemmed |
The Impact of Targeted Data Collection on Nonresponse Bias in an Establishment Survey: A Simulation Study of Adaptive Survey Design |
title_sort |
impact of targeted data collection on nonresponse bias in an establishment survey: a simulation study of adaptive survey design |
publisher |
Sciendo |
series |
Journal of Official Statistics |
issn |
2001-7367 |
publishDate |
2017-09-01 |
description |
Nonresponse rates have been growing over time leading to concerns about survey data quality. Adaptive designs seek to allocate scarce resources by targeting specific subsets of sampled units for additional effort or a different recruitment protocol. In order to be effective in reducing nonresponse, the identified subsets of the sample need two key features: 1) their probabilities of response can be impacted by changing design features, and 2) once they have responded, this can have an impact on estimates after adjustment. The National Agricultural Statistics Service (NASS) is investigating the use of adaptive design techniques in the Crops Acreage, Production, and Stocks Survey (Crops APS). The Crops APS is a survey of establishments which vary in size and, hence, in their potential impact on estimates. In order to identify subgroups for targeted designs, we conducted a simulation study that used Census of Agriculture (COA) data as proxies for similar survey items. Different patterns of nonresponse were simulated to identify subgroups that may reduce estimated nonresponse bias when their response propensities are changed. |
topic |
adaptive survey design nonresponse bias establishment surveys |
url |
https://doi.org/10.1515/jos-2017-0039 |
work_keys_str_mv |
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