Automated Name Selection for the Network Scale-up Method

Adrià Fenoy, Michał Bojanowski, Miranda J. Lubbers
{"title":"Automated Name Selection for the Network Scale-up Method","authors":"Adrià Fenoy, Michał Bojanowski, Miranda J. Lubbers","doi":"10.1177/1525822x241243115","DOIUrl":null,"url":null,"abstract":"To estimate the distribution of the number of acquaintances of the members of a society, the network scale-up method asks survey respondents about the number of people they know with features for which national statistics are available. While many features have been used for this purpose, first names have been suggested to produce particularly low levels of transmission error and recall bias. For this method to be precise, a set of names needs to be selected for the survey that jointly represents the population in relevant variables such as gender or age. This article provides a solution approach to finding the optimal set of names. This can be applied to any population for which a joint distribution of first names and relevant variables is available. We show that our approach successfully provides sets of names closely mirroring the population distributions for six countries with different name statistics.","PeriodicalId":505739,"journal":{"name":"Field Methods","volume":"104 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Field Methods","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/1525822x241243115","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

To estimate the distribution of the number of acquaintances of the members of a society, the network scale-up method asks survey respondents about the number of people they know with features for which national statistics are available. While many features have been used for this purpose, first names have been suggested to produce particularly low levels of transmission error and recall bias. For this method to be precise, a set of names needs to be selected for the survey that jointly represents the population in relevant variables such as gender or age. This article provides a solution approach to finding the optimal set of names. This can be applied to any population for which a joint distribution of first names and relevant variables is available. We show that our approach successfully provides sets of names closely mirroring the population distributions for six countries with different name statistics.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
为网络扩展法自动选择名称
为了估算社会成员熟人数量的分布情况,网络扩展法向调查对象询问他们所认识的人的数量,这些人的特征有国家统计数据可查。虽然有许多特征被用于此目的,但有人认为名字产生的传播误差和回忆偏差特别小。为使这一方法精确,需要为调查选择一组姓名,以共同代表性别或年龄等相关变量的人口。本文提供了一种寻找最佳名称集的解决方法。该方法适用于任何可以获得姓名和相关变量联合分布的人群。我们的研究表明,我们的方法成功地为六个具有不同姓名统计数据的国家提供了与人口分布密切相关的姓名集。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Penciling: An Anonymization Method for Social Media Images Automated Name Selection for the Network Scale-up Method The Identification and Documentation of On-site Sensory and Multisensory Experience–A Methodological Protocol Poverty and Wealth without a Ladder? An Appraisal of the Stages of Progress Method among Agro–Pastoralists in Ethiopia’s Lower Omo Valley How Training Affects Interviewer Performance Over Time: A Field Experiment with a Large-scale National Representative Survey
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1