Analysis of Paired Data.
A common and unfortunate error in statistical analysis is the failure to account for dependencies in the data. In many studies, there is a set of individual participants or experimental objects where two observations are made on each individual or object. This leads to a natural pairing of data. Thi...
| Publicado en: | Prehospital & Disaster Medicine Vol. 40; no. 2; pp. 61 - 64 |
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| Formato: | Journal Article |
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Cambridge University Press
Apr2025
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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=ccm&AN=184677453&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184677453 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1049023X YX6 jtl: Prehospital & Disaster Medicine issn: 1049023X maglogo: N pubinfo: dt: Apr2025 vid: 40 iid: 2 pid: 15979 pub: Cambridge University Press artinfo: ui: 184677453 10.1017/S1049023X25001517 184677453 ppf: 61 ppct: 3 formats: tig: atl: Analysis of Paired Data. aug: au: Franc, Jeffrey Michael affil: Associate Professor, Department of Emergency Medicine, University of Alberta Visiting Professor in Disaster Medicine, Università del Piemonte Orientale Adjunct Faculty, Harvard/BIDMC Disaster Medicine Fellowship sug: ab: A common and unfortunate error in statistical analysis is the failure to account for dependencies in the data. In many studies, there is a set of individual participants or experimental objects where two observations are made on each individual or object. This leads to a natural pairing of data. This editorial discusses common situations where paired data arises and gives guidance on selecting the correct analysis plan to avoid statistical errors. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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