Can a measurement error perspective improve estimation in neighborhood effects research? A hierarchical Bayesian methodology.
Objective: Neighborhood effects research often employs aggregate data at small geographic areas to understand neighborhood processes. This article investigates whether empirical applications of neighborhood effects research benefit from a measurement error perspective. Methods: The article situates...
| Publicado en: | Social Science Quarterly (Wiley-Blackwell) Vol. 103; no. 5; pp. 1260 - 1273 |
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| Autores principales: | , |
| Formato: | Artículo |
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Wiley-Blackwell
Sep2022
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| 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=159689198&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 159689198 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00384941 SSQ jtl: Social Science Quarterly (Wiley-Blackwell) issn: 00384941 maglogo: Y pubinfo: dt: Sep2022 vid: 103 iid: 5 pid: 480 pub: Wiley-Blackwell artinfo: ui: 159689198 10.1111/ssqu.13190 ppf: 1260 ppct: 13 formats: tig: atl: Can a measurement error perspective improve estimation in neighborhood effects research? A hierarchical Bayesian methodology. aug: au: Mayer, Duncan J. Fischer, Robert L. affil: Jack Joseph and Morton Mandel School of Applied Social Sciences, Case Western Reserve University, Cleveland Ohio, , USA su: Detroit (Mich.) Neighborhoods Measurement errors Sampling (Process) sug: subj: Neighborhoods Detroit (Mich.) Measurement errors Sampling (Process) keyword: Bayesian inference census child welfare crime measurement error neighborhood effects Bayesian inference census child welfare crime measurement error neighborhood effects ab: Objective: Neighborhood effects research often employs aggregate data at small geographic areas to understand neighborhood processes. This article investigates whether empirical applications of neighborhood effects research benefit from a measurement error perspective. Methods: The article situates neighborhood effects research in a measurement error framework and then details a Bayesian methodology capable of addressing measurement concerns. We compare the proposed model to conventional linear models on crime data from Detroit, Michigan, as well as two simulated examples that closely mirror the sampling process. Results: The Detroit data example shows that the proposed model makes substantial differences to parameters of interest and reduces the mean squared error. The simulations confirm the benefit of the proposed model, regularly recovering parameters and conveying uncertainty where conventional linear models fail. Conclusion: A measurement error perspective can improve estimation for data aggregated at small geographic areas. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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