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Almirall says comparing SMART designs will increase treatment quality for children with autism

Thompson says America must "unchoose" policies that have led to mass incarceration

Alter says lack of access to administrative data is "big drag on research"


Knodel honored by Thailand's Chulalongkorn University

Susan Murphy to speak at U-M kickoff for data science initiative, Oct 6, Rackham

Andrew Goodman-Bacon, former trainee, wins 2015 Nevins Prize for best dissertation in economic history

Deirdre Bloome wins ASA award for work on racial inequality and intergenerational transmission

Next Brown Bag

Monday, Oct 12 at noon, 6050 ISR
Joe Grengs: Policy & planning for transportation equity

George C. Alter photo

Demographic Analysis of Longitudinal Historical Data

a PSC Research Project

Investigators:   George C. Alter, Susan Hautaniemi Leonard

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.

Funding Period: 02/14/2009 to 04/30/2015

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