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Human Subject Protection and Disclosure Risk Analysis

Human Subject Protection and Disclosure Risk Analysis
主体保护和披露风险分析
批准号:
7359657
负责人:
JAMES W MCNALLY
金额:
$103.61万
依托单位国家:
美国
项目类别:
财政年份:
2004
资助国家:
美国
项目状态:
已结题
起止时间:
2004-02-23 至 2009-12-31

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中文摘要
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英文摘要
HUMAN SUBJECT PROTECTION AND DISCLOSURE RISK ANALYSIS DESCRIPTION (provided by applicant): This research program aims to answer the call for knowledge and innovation that has arisen out of recent events and inquiries about studies involving human subjects. Analyses underway now at influential public institutions such as the National Academy of Sciences and the Institute of Medicine are likely to call for new research on the risks of human research. In the area of social science research, we expect those calls for research to emphasize the reduction of disclosure risk and the creation of new responsibilities at data archives. The Survey Research Center and the Inter-university Consortium for Political and Social Research at the University of Michigan propose four closely related projects related to the protection of human subjects through disclosure risk analysis and disclosure limitation. These projects specifically address the following topics: I. Informed Consent and Perceptions of Risk and Harm in Survey Participation II. Estimation of Disclosure Risk and Statistical Methods for Disclosure Limitation III. Statistical Disclosure Control: Best Practices and Tools for the Social Sciences IV. Resources for the Secure Dissemination of Human Subjects Data The results of this program of research will be new insights into disclosure risk and its elimination, and new procedures and best practices for accomplishing these goals, along with public programs and training to disseminate the knowledge.
期刊论文(2)
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科研奖励(0)
会议论文
A SEMIPARAMETRIC MULTIPLE IMPUTATION APPROACH TO FULLY SYNTHETIC DATA FOR COMPLEX SURVEYS.
用于复杂调查的完全合成数据的半参数多重插补方法。
DOI: 10.1093/jssam/smac016
发表时间: 2022
期刊: Journal of survey statistics and methodology
影响因子: 2.1
作者: [Yu,Mandi, He,Yulei, Raghunathan,TrivelloreE]
通讯作者: Raghunathan,TrivelloreE
Informed Consent for Web Paradata Use.
Web Paradata 使用的知情同意。
DOI: --
发表时间: 2013
期刊: Survey research methods
影响因子: 4.8
作者: [Couper,MickP, Singer,Eleanor]
通讯作者: Singer,Eleanor
FACTORS IN AGING: Best Practices in Archiving and Sharing Interoperable Longitudinal Data Resources on Aging
FACTORS IN AGING: Best Practices in Archiving and Sharing Interoperable Longitudinal Data Resources on Aging
Creating an Interoperability Data Infrastructure for Research on the Aging Lifecourse
FACTORS IN AGING: Best Practices in Archiving and Sharing Longitudinal Data Resources on Aging
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