Thirty-three myths and misconceptions about population data: from data capture and processing to linkage.

IF 1.6 Q3 HEALTH CARE SCIENCES & SERVICES
Peter Christen, Rainer Schnell
{"title":"Thirty-three myths and misconceptions about population data: from data capture and processing to linkage.","authors":"Peter Christen,&nbsp;Rainer Schnell","doi":"10.23889/ijpds.v8i1.2115","DOIUrl":null,"url":null,"abstract":"<p><p>Databases covering all individuals of a population are increasingly used for research and decision-making. The massive size of such databases is often mistaken as a guarantee for valid inferences. However, population data have characteristics that make them challenging to use. Various assumptions on population coverage and data quality are commonly made, including how such data were captured and what types of processing have been applied to them. Furthermore, the full potential of population data can often only be unlocked when such data are linked to other databases. Record linkage often implies subtle technical problems, which are easily missed. We discuss a diverse range of myths and misconceptions relevant for anybody capturing, processing, linking, or analysing population data. Remarkably, many of these myths and misconceptions are due to the social nature of data collections and are therefore missed by purely technical accounts of data processing. Many are also not well documented in scientific publications. We conclude with a set of recommendations for using population data.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"8 1","pages":"2115"},"PeriodicalIF":1.6000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/b0/03/ijpds-08-2115.PMC10454001.pdf","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Population Data Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23889/ijpds.v8i1.2115","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 3

Abstract

Databases covering all individuals of a population are increasingly used for research and decision-making. The massive size of such databases is often mistaken as a guarantee for valid inferences. However, population data have characteristics that make them challenging to use. Various assumptions on population coverage and data quality are commonly made, including how such data were captured and what types of processing have been applied to them. Furthermore, the full potential of population data can often only be unlocked when such data are linked to other databases. Record linkage often implies subtle technical problems, which are easily missed. We discuss a diverse range of myths and misconceptions relevant for anybody capturing, processing, linking, or analysing population data. Remarkably, many of these myths and misconceptions are due to the social nature of data collections and are therefore missed by purely technical accounts of data processing. Many are also not well documented in scientific publications. We conclude with a set of recommendations for using population data.

Abstract Image

Abstract Image

关于人口数据的33个迷思和误解:从数据获取和处理到联系。
涵盖人口中所有个人的数据库越来越多地用于研究和决策。这类数据库的庞大规模常常被误认为是有效推断的保证。然而,人口数据的特点使其难以使用。通常会对人口覆盖范围和数据质量作出各种假设,包括如何获取这些数据以及对这些数据进行了何种处理。此外,人口数据的全部潜力往往只有在这些数据与其他数据库相联系时才能发挥出来。记录链接通常意味着微妙的技术问题,这些问题很容易被忽略。我们讨论了与捕获、处理、链接或分析人口数据相关的各种各样的神话和误解。值得注意的是,许多这些神话和误解是由于数据收集的社会性质,因此被数据处理的纯技术描述所忽略。许多也没有在科学出版物中得到很好的记录。最后,我们提出了一组使用人口数据的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
2.50
自引率
0.00%
发文量
386
审稿时长
20 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信