Validating an Index of Selection Bias for Proportions in Non‐Probability Samples

Author:

Hammon Angelina12ORCID,Zinn Sabine13

Affiliation:

1. German Socio‐Economic Panel Study Department Berlin Germany

2. Chair of Statistics and Econometrics University of Bamberg Bamberg Germany

3. Department of Social Sciences Humboldt University Berlin Germany

Abstract

SummaryFast online surveys without sampling frames are becoming increasingly important in survey research. Their recruitment methods result in non‐probability samples. As the mechanism of data generation is always unknown in such samples, the problem of non‐ignorability arises making vgeneralisation of calculated statistics to the population of interest highly questionable. Sensitivity analyses provide a valuable tool to deal with non‐ignorability. They capture the impact of different sample selection mechanisms on target statistics. In 2019, Andridge and colleagues proposed an index to quantify potential (non‐ignorable) selection bias in proportions that combines the effects of different selection mechanisms. In this paper, we validate this index with an artificial non‐probability sample generated from a large empirical data set and additionally applied it to proportions estimated from data on current political attitudes arising from a real non‐probability sample selected via River sampling. We find a number of conditions that must be met for the index to perform meaningfully. When these requirements are fulfilled, the index shows an overall good performance in both of our applications in detecting and correcting present selection bias in estimated proportions. Thus, it provides a powerful measure for evaluating the robustness of results obtained from non‐probability samples.

Publisher

Wiley

Reference28 articles.

1. American Association for Public Opinion Research2013.Report of the AAPOR Task Force on Non‐Probability Sampling.https://www.aapor.org/AAPOR_Main/media/MainSiteFiles/NPS_TF_Report_Final_7_revised_FNL_6_22_13.pdf

2. Andridge R.R.(2009).Statistical methods for missing data in complex sample surveys. Ph.D. Thesis University of Michigan.

3. Using proxy pattern‐mixture models to explain bias in estimates of COVID‐19 vaccine uptake from two large surveys;Andridge R.R.;J. R. Stat. Soc. Ser. A: Stat. Soc.,2024

4. Andridge R.R.&Little RJA(2009).Extensions of Proxy Pattern‐Mixture Analysis for Survey Nonresponse. InJoint Statistical Meetings (JSM) Proceedings Section on Survey Research Methods pp.2468–2482.

5. Proxy pattern‐mixture analysis for survey nonresponse;Andridge R.R.;J. Off. Stat.,2011

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3