Spatial Variation in the Quality of American Community Survey Estimates.
Social science research, public and private sector decisions, and allocations of federal resources often rely on data from the American Community Survey (ACS). However, this critical data source has high uncertainty in some of its most frequently used estimates. Using 2006-2010 ACS median household...
| Publicado en: | Demography (Springer Nature) Vol. 53; no. 5; pp. 1535 - 1555 |
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| Autores principales: | , , , , , |
| Formato: | journal article |
| Publicado: |
Springer Nature
Oct2016
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=118508001&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 118508001 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00703370 DEM jtl: Demography (Springer Nature) issn: 00703370 maglogo: N pubinfo: dt: Oct2016 vid: 53 iid: 5 pid: 237 pub: Springer Nature artinfo: ui: 118508001 10.1007/s13524-016-0499-1 ppf: 1535 ppct: 20 formats: fmt: @attributes: type: P size: 1.5MB tig: atl: Spatial Variation in the Quality of American Community Survey Estimates. aug: au: Folch, David Arribas-Bel, Daniel Koschinsky, Julia Spielman, Seth Folch, David C Spielman, Seth E affil: Department of Geography , Florida State University , Tallahassee USA Department of Geography and Planning , University of Liverpool , Liverpool UK Center for Spatial Data Science , University of Chicago , Chicago USA Department of Geography , University of Colorado at Boulder , Boulder USA Department of Geography, Florida State University, Tallahassee, FL, USA Department of Geography, University of Colorado at Boulder, Boulder, CO, USA su: United States Social surveys Income Socioeconomic factors Cross-sectional method Spatial variation Quality control Research management Social statistics Experimental design Statistics sug: subj: Social surveys Income Socioeconomic factors Cross-sectional method United States Spatial variation Quality control Research management Social statistics Experimental design Statistics keyword: American Community Survey Data uncertainty Income estimates Margin of error Spatial analysis American Community Survey Data uncertainty Income estimates Margin of error Spatial analysis ab: Social science research, public and private sector decisions, and allocations of federal resources often rely on data from the American Community Survey (ACS). However, this critical data source has high uncertainty in some of its most frequently used estimates. Using 2006-2010 ACS median household income estimates at the census tract scale as a test case, we explore spatial and nonspatial patterns in ACS estimate quality. We find that spatial patterns of uncertainty in the northern United States differ from those in the southern United States, and they are also different in suburbs than in urban cores. In both cases, uncertainty is lower in the former than the latter. In addition, uncertainty is higher in areas with lower incomes. We use a series of multivariate spatial regression models to describe the patterns of association between uncertainty in estimates and economic, demographic, and geographic factors, controlling for the number of responses. We find that these demographic and geographic patterns in estimate quality persist even after we account for the number of responses. Our results indicate that data quality varies across places, making cross-sectional analysis both within and across regions less reliable. Finally, we present advice for data users and potential solutions to the challenges identified. pubtype: Academic Journal doctype: journal article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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