Demographic Analysis of Longitudinal Historical Data

纵向历史数据的人口统计分析

• 批准号: 7849487
• 负责人: GEORGE C. ALTER
• 金额: $ 8.51万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-06-01 至 2014-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7849487
• 关键词: Age; Aging; Award; Back; Complex; Computer software; Data; Data Base Management; Data Set; Databases; Demographer; Demographic Analyses; Demographic Transitions; Dimensions; Educational process of instructing; Evaluation; Event; Event History Analysis; Faculty; Family; Family Reconstitutions; Far East; Fertility; Funding; Funding Mechanisms; Genealogy; Grant; Historical Demography; Household; Individual; International; Learning; Logic; Mainstreaming; Methodology; Methods; Modeling; NIH Program Announcements; Parish Registers; Population Registers; Population Research; Population Study; Procedures; Process; Recording of previous events; Research; Research Personnel; Rest; Source; Statistical Methods; Structure; Students; System; Techniques; Time; Training; Training Programs; United States National Institutes of Health; Work; base; data management; design; expectation; experience; fetal programming; interdisciplinary approach; interdisciplinary collaboration; life history; longitudinal analysis; migration; mortality; programs; public health relevance; skills; theories; training project

DESCRIPTION (provided by applicant): This project is a competing renewal for grant number R25 HD049479-01, which will provide extremely valuable specialized training in historical demographic techniques for analyzing longitudinal data to students and researchers working in a variety of demographic sub-fields. The rational for the project is simple: Historical demography has a long history of important contributions to the theory, methods, and practice of population studies, especially in the use of longitudinal data. Historical demographers are currently making important contributions to mainstream demographic research in fertility, mortality, family systems, aging, and migration. Indeed, the size, scope, and temporal and geographic coverage of databases currently available and under construction are unprecedented. Since historical data are often longitudinal and multi-level, they raise subtle methodological problems. Meaningful analysis often requires specialized methodologies, such as family reconstitution and back projection that are unique to historical research. Since they are based on fundamental principles of demographic theory, students trained in these methods are both prepared for historical research and better able to use complex contemporary sources. Historical data can be a perfect model for analysis of demographic processes. The number of observed covariates is usually limited, and historical demographers have excelled in creatively using longitudinal and genealogical information to construct contextual and time-varying covariates. The longitudinal analysis techniques students learn will provide a roadmap for use with any data set with a time dimension, including many large contemporary data sets collected through NIH funding. This program will offer both formal courses and opportunities for practical experience with active researchers. Students will be introduced to data sets and advanced statistical techniques at the forefront of current research. PUBLIC HEALTH RELEVANCE: This project will continue an interdisciplinary project that trains students, researchers and faculty in the use and analysis of historical longitudinal demographic data, previously supported by grant number R25 HD049479-01. This program will enable and enhance demographic research using advanced statistical techniques in fertility, mortality, family systems, aging, and migration, using new and existing historical and contemporary datasets, many of them collected through NIH funding mechanisms.

描述（由申请人提供）：本项目为资助号为R25 HD049479-01的竞争性更新项目，将为从事各种人口统计子领域工作的学生和研究人员提供非常有价值的历史人口统计技术纵向数据分析专业培训。该项目的理由很简单：历史人口学在人口研究的理论、方法和实践方面有着悠久的重要贡献，特别是在使用纵向数据方面。历史人口统计学家目前在生育率、死亡率、家庭制度、老龄化和移民等主流人口研究方面做出了重要贡献。事实上，目前可用和正在建设的数据库的规模、范围、时间和地理覆盖范围都是前所未有的。由于历史数据往往是纵向的和多层次的，它们提出了微妙的方法问题。有意义的分析通常需要专门的方法，例如历史研究特有的家庭重构和回溯预测。由于这些方法是基于人口理论的基本原理，因此接受这些方法训练的学生既能为历史研究做好准备，也能更好地利用复杂的当代资料来源。历史数据可以作为分析人口统计过程的完美模型。观察到的协变量的数量通常是有限的，历史人口统计学家擅长创造性地使用纵向和家谱信息来构建上下文和时变的协变量。学生学习的纵向分析技术将提供一个路线图，用于任何具有时间维度的数据集，包括通过NIH资助收集的许多大型当代数据集。该项目将提供正式的课程和与活跃的研究人员进行实践体验的机会。学生将被介绍到当前研究前沿的数据集和先进的统计技术。