Demographic Analysis of Longitudinal Historical Data

纵向历史数据的人口统计分析

• 批准号: 6901273
• 负责人: Myron P. Gutmann
• 金额: $ 14.23万
• 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 2007-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6901273
• 关键词: aging; curriculum; data management; health science research; human data; human migration; human population composition; human population dynamics; longitudinal human study; reproduction; science education; statistics /biometry; training

DESCRIPTION (provided by applicant): This project 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. We also aim to secure and archive some of the classic data sets used in earlier research and to make them available for analysis with the most modern methods.

描述（由申请人提供）：该项目将在历史人口统计学技术中提供极为有价值的专业培训，以分析在各种人口统计学子场工作的学生和研究人员分析纵向数据。该项目的理性很简单：历史人口统计学对人群研究的理论，方法和实践有重要贡献的悠久历史，尤其是在使用纵向数据方面。历史人口统计学家目前正在为生育，死亡率，家庭系统，衰老和移民的主流人口研究做出重要贡献。实际上，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是前所未有的。由于历史数据通常是纵向和多层次的，因此会引起微妙的方法论问题。有意义的分析通常需要专业的方法论，例如家庭重建和背部投影，这是历史研究所独有的。由于它们基于人口统计学理论的基本原则，因此接受了这些方法培训的学生都准备了历史研究，并且能够更好地使用复杂的当代资料。历史数据可以是分析人口过程的理想模型。观察到的协变量的数量通常受到限制，历史人口统计学家在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方面表现出色。学生学习的纵向分析技术将为任何具有时间维度的数据集提供路线图，包括许多通过NIH资助收集的大型现代数据集。该计划将为积极的研究人员提供正式的课程和实践经验的机会。将向学生介绍当前研究最前沿的数据集和高级统计技术。我们还旨在保护和归档早期研究中使用的一些经典数据集，并以最现代的方法进行分析。