Analysis of Household Pulse Survey Public-Use Microdata via Unit-Level Models for Informative Sampling
The Household Pulse Survey, recently released by the U.S. Census Bureau, gathers information about the respondents’ experiences regarding employment status, food security, housing, physical and mental health, access to health care, and education disruption. Design-based estimates are produced for al...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Stats |
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| Online Access: | https://www.mdpi.com/2571-905X/5/1/10 |
| _version_ | 1797442035954745344 |
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| author | Alexander Sun Paul A. Parker Scott H. Holan |
| author_facet | Alexander Sun Paul A. Parker Scott H. Holan |
| author_sort | Alexander Sun |
| collection | DOAJ |
| description | The Household Pulse Survey, recently released by the U.S. Census Bureau, gathers information about the respondents’ experiences regarding employment status, food security, housing, physical and mental health, access to health care, and education disruption. Design-based estimates are produced for all 50 states and the District of Columbia (DC), as well as 15 Metropolitan Statistical Areas (MSAs). Using public-use microdata, this paper explores the effectiveness of using unit-level model-based estimators that incorporate spatial dependence for the Household Pulse Survey. In particular, we consider Bayesian hierarchical model-based spatial estimates for both a binomial and a multinomial response under informative sampling. Importantly, we demonstrate that these models can be easily estimated using Hamiltonian Monte Carlo through the Stan software package. In doing so, these models can readily be implemented in a production environment. For both the binomial and multinomial responses, an empirical simulation study is conducted, which compares spatial and non-spatial models. Finally, using public-use Household Pulse Survey micro-data, we provide an analysis that compares both design-based and model-based estimators and demonstrates a reduction in standard errors for the model-based approaches. |
| first_indexed | 2024-03-09T12:35:39Z |
| format | Article |
| id | doaj.art-2b2bc9b849164e28809c6ce3a921d6f6 |
| institution | Directory Open Access Journal |
| issn | 2571-905X |
| language | English |
| last_indexed | 2024-03-09T12:35:39Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Stats |
| spelling | doaj.art-2b2bc9b849164e28809c6ce3a921d6f62023-11-30T22:24:03ZengMDPI AGStats2571-905X2022-02-015113915310.3390/stats5010010Analysis of Household Pulse Survey Public-Use Microdata via Unit-Level Models for Informative SamplingAlexander Sun0Paul A. Parker1Scott H. Holan2Department of Statistics, University of Missouri, 146 Middlebush Hall, Columbia, MO 65211, USADepartment of Statistics, University of California Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USADepartment of Statistics, University of Missouri, 146 Middlebush Hall, Columbia, MO 65211, USAThe Household Pulse Survey, recently released by the U.S. Census Bureau, gathers information about the respondents’ experiences regarding employment status, food security, housing, physical and mental health, access to health care, and education disruption. Design-based estimates are produced for all 50 states and the District of Columbia (DC), as well as 15 Metropolitan Statistical Areas (MSAs). Using public-use microdata, this paper explores the effectiveness of using unit-level model-based estimators that incorporate spatial dependence for the Household Pulse Survey. In particular, we consider Bayesian hierarchical model-based spatial estimates for both a binomial and a multinomial response under informative sampling. Importantly, we demonstrate that these models can be easily estimated using Hamiltonian Monte Carlo through the Stan software package. In doing so, these models can readily be implemented in a production environment. For both the binomial and multinomial responses, an empirical simulation study is conducted, which compares spatial and non-spatial models. Finally, using public-use Household Pulse Survey micro-data, we provide an analysis that compares both design-based and model-based estimators and demonstrates a reduction in standard errors for the model-based approaches.https://www.mdpi.com/2571-905X/5/1/10Hamiltonian Monte CarloICARsmall area estimationspatialStan |
| spellingShingle | Alexander Sun Paul A. Parker Scott H. Holan Analysis of Household Pulse Survey Public-Use Microdata via Unit-Level Models for Informative Sampling Stats Hamiltonian Monte Carlo ICAR small area estimation spatial Stan |
| title | Analysis of Household Pulse Survey Public-Use Microdata via Unit-Level Models for Informative Sampling |
| title_full | Analysis of Household Pulse Survey Public-Use Microdata via Unit-Level Models for Informative Sampling |
| title_fullStr | Analysis of Household Pulse Survey Public-Use Microdata via Unit-Level Models for Informative Sampling |
| title_full_unstemmed | Analysis of Household Pulse Survey Public-Use Microdata via Unit-Level Models for Informative Sampling |
| title_short | Analysis of Household Pulse Survey Public-Use Microdata via Unit-Level Models for Informative Sampling |
| title_sort | analysis of household pulse survey public use microdata via unit level models for informative sampling |
| topic | Hamiltonian Monte Carlo ICAR small area estimation spatial Stan |
| url | https://www.mdpi.com/2571-905X/5/1/10 |
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