Adjusting for Design Effects in Disproportionate Stratified Sampling Designs Through Weighting.
This article validates the necessity of adjusting for the design effects in disproportionate stratified sampling designs through the use of sample weights. Using data from the 1958 Birth Cohort study, we demonstrate that complex sampling designs introduce sampling error and even sampling bias into s...
| Published in: | Crime & Delinquency Vol. 60; no. 2; pp. 306 - 326 |
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| Main Authors: | , |
| Format: | Article |
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Sage Publications Inc.
Mar2014
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=94586833&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 94586833 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00111287 CRD jtl: Crime & Delinquency issn: 00111287 maglogo: Y pubinfo: dt: Mar2014 vid: 60 iid: 2 pid: 344 pub: Sage Publications Inc. artinfo: ui: 94586833 10.1177/0011128714522114 ppf: 306 ppct: 20 formats: tig: atl: Adjusting for Design Effects in Disproportionate Stratified Sampling Designs Through Weighting. aug: au: Tracy, Paul E. Carkin, Danielle Marie affil: University of Massachusetts Lowell, USA su: Vital records (Births, deaths, etc.) Population research Criminology Proportional representation Data analysis Sampling errors Parameter estimation sug: subj: Vital records (Births, deaths, etc.) Population research Criminology Proportional representation Data analysis Sampling errors Parameter estimation keyword: sampling bias sampling error sampling weights stratified sampling sampling bias sampling error sampling weights stratified sampling ab: This article validates the necessity of adjusting for the design effects in disproportionate stratified sampling designs through the use of sample weights. Using data from the 1958 Birth Cohort study, we demonstrate that complex sampling designs introduce sampling error and even sampling bias into sample data. Such sample data are a poor representation of population parameters. These design effects can be addressed through the application of sample weights. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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