NLSY Kinship Links: Reliable and Valid Sibling Identification

NLSY 亲属关系链接:可靠且有效的兄弟姐妹身份识别

基本信息

  • 批准号:
    8242672
  • 负责人:
  • 金额:
    $ 22.2万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2011
  • 资助国家:
    美国
  • 起止时间:
    2011-05-01 至 2014-04-30
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): The project is designed to use sophisticated data management methods to develop new NLSY kinship linking files, which will be broadly valuable to demographic, developmental, and behavior genetic researchers, as well as to broader social/behavioral science researchers. We propose to create data files from newly available kinship information in two of the National Longitudinal Survey databases, the National Longitudinal Survey of Youth 1979 data (the NLSY79, a probability sample of adolescents aged 14-22 in 1979), and the National Longitudinal Survey of Youth Children data (the NLSYC, a survey of all biological children born to females in the NLSY79). We will create sibling pair files and multi-level files using the NLSY79 and the NLSYC. Our extensive background creating kinship linking files using implicit kinship links in the three past NIH-funded projects provides us unique experience and talent to develop these files, to circulate them to research teams, and to provide technical support to facilitate their use in applied research settings. The product of this effort will be the production of four kinship link files in a variety of formats, a web-based system available to access the files, and training of a new young researcher to provide technical support in the use of these files. We document the value of these files to dozens, and potentially hundreds, of research teams who conduct family research using the NLSY datasets. PUBLIC HEALTH RELEVANCE: This project involves filling in some critical data gaps in the National Longitudinal Survey of Youth (NLSY79 and NLSY-Children) data, to support research being conducted on many NLSY health behaviors by research teams all over the world. These are both datasets based on family/sibling designs, which can be used to study family influences on health behaviors from many perspectives. But the general "sibling" category is not informative as to type of sibling. Recently, new survey questions have been added, which can help us distinguish the sibling type for the over 12,500 respondents in the NLSY79 survey, and the over 13,000 children in the NLSY-Children survey. We will develop sibling links to distinguish between identical twins, fraternal twins, full, half, and adoptive siblings so that research can be conducted by demographic, developmental, and behavior genetic researchers who focus on family studies.
该项目旨在使用复杂的数据管理方法来开发新的NLSY亲属关系链接文件,这对人口统计学,发育和行为遗传学研究人员以及更广泛的社会/行为科学研究人员具有广泛的价值。我们建议从两个国家纵向调查数据库中新获得的亲属关系信息创建数据文件,即1979年国家青年纵向调查数据(NLSY 79,1979年14-22岁青少年的概率样本)和国家青年儿童纵向调查数据(NLSYC,NLSY 79中女性所生所有生物学子女的调查)。我们将使用NLSY 79和NLSYC创建同级对文件和多级文件。我们在过去三个NIH资助的项目中使用隐式亲属关系创建亲属关系链接文件的广泛背景为我们提供了独特的经验和人才来开发这些文件,将其分发给研究团队,并提供技术支持以促进其在应用研究环境中的使用。这一努力的成果将是制作四个不同格式的亲属关系档案,一个可供查阅这些档案的网络系统,以及培训一名新的年轻研究人员,在使用这些档案方面提供技术支持。我们记录了这些文件的价值,数十个,可能是数百个,使用NLSY数据集进行家庭研究的研究团队。 公共卫生相关性:该项目涉及填补国家青年纵向调查(NLSY 79和NLSY-儿童)数据中的一些关键数据空白,以支持世界各地研究团队对许多NLSY健康行为进行的研究。这两个数据集都是基于家庭/兄弟姐妹设计的,可以从多个角度研究家庭对健康行为的影响。但是一般的“兄弟姐妹”类别并不能说明兄弟姐妹的类型。最近,我们增加了新的调查问题,可以帮助我们区分NLSY 79调查中超过12,500名受访者和NLSY儿童调查中超过13,000名儿童的兄弟姐妹类型。我们将发展兄弟姐妹的联系,以区分同卵双胞胎,异卵双胞胎,全,半,和收养兄弟姐妹,使研究可以进行人口统计学,发育和行为遗传学研究人员谁专注于家庭研究。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Joseph L. Rodgers其他文献

Psychosocial Predictors of Adolescent Sexual Risk Behavior: A Quasi-Experimental Analysis in a Nationally Representative Sample of American Youths
  • DOI:
    10.1016/j.jadohealth.2013.10.046
  • 发表时间:
    2014-02-01
  • 期刊:
  • 影响因子:
  • 作者:
    Kelly Donahue;Carol Van Hulle;Joseph L. Rodgers;Brian M. D'Onofrio
  • 通讯作者:
    Brian M. D'Onofrio

Joseph L. Rodgers的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Joseph L. Rodgers', 18)}}的其他基金

New NLSY Kinship Links and Longitudinal/Cross-Generational Models: Cognition and Fertility Research
新的 NLSY 亲属关系和纵向/跨代模型:认知和生育力研究
  • 批准号:
    9239744
  • 财政年份:
    2017
  • 资助金额:
    $ 22.2万
  • 项目类别:
NLSY Kinship Links: Reliable and Valid Sibling Identification
NLSY 亲属关系链接:可靠且有效的兄弟姐妹身份识别
  • 批准号:
    8463419
  • 财政年份:
    2011
  • 资助金额:
    $ 22.2万
  • 项目类别:
NLSY Kinship Links: Reliable and Valid Sibling Identification
NLSY 亲属关系链接:可靠且有效的兄弟姐妹身份识别
  • 批准号:
    8108424
  • 财政年份:
    2011
  • 资助金额:
    $ 22.2万
  • 项目类别:
Biometrical Modeling of Fertility Using the NLSY
使用 NLSY 进行生育力生物统计建模
  • 批准号:
    6773220
  • 财政年份:
    2003
  • 资助金额:
    $ 22.2万
  • 项目类别:
Biometrical Modeling of Fertility Using the NLSY
使用 NLSY 进行生育力生物统计建模
  • 批准号:
    6685779
  • 财政年份:
    2003
  • 资助金额:
    $ 22.2万
  • 项目类别:
Biometrical Modeling of Fertility Using the NLSY
使用 NLSY 进行生育力生物统计建模
  • 批准号:
    7079300
  • 财政年份:
    2003
  • 资助金额:
    $ 22.2万
  • 项目类别:
Biometrical Modeling of Fertility Using the NLSY
使用 NLSY 进行生育力生物统计建模
  • 批准号:
    6923560
  • 财政年份:
    2003
  • 资助金额:
    $ 22.2万
  • 项目类别:
ADOLESCENT BEHAVIOR: FAMILY AND NON-FAMILY INFLUENCES
青少年行为:家庭和非家庭的影响
  • 批准号:
    2198393
  • 财政年份:
    1987
  • 资助金额:
    $ 22.2万
  • 项目类别:
ADOLESCENT BEHAVIOR: FAMILY AND NON-FAMILY INFLUENCES
青少年行为:家庭和非家庭的影响
  • 批准号:
    6324019
  • 财政年份:
    1987
  • 资助金额:
    $ 22.2万
  • 项目类别:
ADOLESCENT BEHAVIOR: FAMILY AND NON-FAMILY INFLUENCES
青少年行为:家庭和非家庭的影响
  • 批准号:
    2888929
  • 财政年份:
    1987
  • 资助金额:
    $ 22.2万
  • 项目类别:

相似海外基金

CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
  • 批准号:
    2337776
  • 财政年份:
    2024
  • 资助金额:
    $ 22.2万
  • 项目类别:
    Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
  • 批准号:
    2338816
  • 财政年份:
    2024
  • 资助金额:
    $ 22.2万
  • 项目类别:
    Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
  • 批准号:
    2338846
  • 财政年份:
    2024
  • 资助金额:
    $ 22.2万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
  • 批准号:
    2348261
  • 财政年份:
    2024
  • 资助金额:
    $ 22.2万
  • 项目类别:
    Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
  • 批准号:
    2348346
  • 财政年份:
    2024
  • 资助金额:
    $ 22.2万
  • 项目类别:
    Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
  • 批准号:
    2348457
  • 财政年份:
    2024
  • 资助金额:
    $ 22.2万
  • 项目类别:
    Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
  • 批准号:
    2404989
  • 财政年份:
    2024
  • 资助金额:
    $ 22.2万
  • 项目类别:
    Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
  • 批准号:
    2339310
  • 财政年份:
    2024
  • 资助金额:
    $ 22.2万
  • 项目类别:
    Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
  • 批准号:
    2339669
  • 财政年份:
    2024
  • 资助金额:
    $ 22.2万
  • 项目类别:
    Continuing Grant
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
  • 批准号:
    EP/Y029089/1
  • 财政年份:
    2024
  • 资助金额:
    $ 22.2万
  • 项目类别:
    Research Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了