Historical and Longitudinal Data: Understanding the IPUMS Ancestry Full Count Data

IPUMS Ancestry Full Count Data is the world’s largest public use demographic data source, comprising the entire population of the contiguous U.S. over the course of 100 years with 816 million observations. These individuals have been linked between each census accounting for 176 million unique individuals between 1850 and 1950. The data provide core information about family and neighborhood characteristics, ethnicity, education, economic activities, migration, and disabilities for the U.S. population between 1850 and 1950. This rich resource underwent a long process from the original enumerations to the data available to researchers. Understanding the strengths and limitations of the data produced within the context of this history are important for properly analyzing the data for today’s research questions. This presentation will cover the history of this data production, considerations for how to explore this resource responsibly, and best practices for researchers to efficiently investigate the data.

What You Will Gain:

• A clear understanding of the historical and contemporary processes behind the creation of the IPUMS Ancestry Full Count Data.
  
• Insight into the strengths, confines, and appropriate uses of linked historical census data.
  
• Practical strategies for responsibly exploring and analyzing large scale historical and longitudinal datasets.

If you need any accommodations to participate, please let us know at least one week prior to the event. Thank you.

Matt Nelson

Matt Nelson is a Senior Research Scientist at the Institute for Social Research and Data Innovation at the University of Minnesota. His work focuses on the development and harmonization of the IPUMS Full Count Census Data (1790–1950) and supporting automated record linkage for the IPUMS Multigenerational Longitudinal Panel. His research interests include historical census data quality and methods for identifying family and kinship networks in large-scale demographic data.