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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

项目摘要

项目成果

JAMES W MCNALLY的其他基金

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中文摘要
翻译
人类主体保护和披露风险分析 描述(由申请人提供):该研究计划旨在回应最近发生的事件和涉及人类受试者的研究的询问所产生的对知识和创新的需求。美国国家科学院和医学研究所等有影响力的公共机构目前正在进行的分析可能需要对人类研究的风险进行新的研究。在社会科学研究领域,我们期望这些研究呼吁能够强调减少披露风险和在数据档案方面建立新的责任。密歇根大学调查研究中心和大学间政治与社会研究联盟提出了四个密切相关的项目,这些项目与通过披露风险分析和披露限制来保护人类受试者有关。这些项目具体涉及以下主题: I. 调查参与中的知情同意以及对风险和伤害的看法 二.披露风险的估计和披露限制的统计方法 三.统计披露控制:社会科学的最佳实践和工具 四.用于安全传播人类受试者数据的资源 该研究计划的结果将是对披露风险及其消除的新见解,以及实现这些目标的新程序和最佳实践,以及传播知识的公共计划和培训。
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)
专著(0)
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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