This webinar is part of the Power of Population Data Science Series
Data used in the administration of public or private programs can be a powerful resource to guide evaluation and planning. Understanding different aspects of administrative data quality is critical for informed use of such data files for analyses. This presentation follows up on the previous Power of Population Data Science Webinar that introduced the Data File Orientation Toolkit as a resource to provide a framework and code for readily enabling data quality evaluation of administrative data sources. This presentation will share NORC at the University of Chicago’s data quality assessment tools, actively demonstrate how to apply the tools to administrative data files, and provide practical guidance for data quality assessment. We will show how these tools can assist researchers with exploring a data file and examining different dimensions of data quality, including checks on data accuracy, the completeness of the records, and the comparability of the data over time and among subgroups of interest.
Note: This presentation was rescheduled from Oct 14th to a date when NORC’s updated data quality assessment tools are anticipated to be available.
View recorded presentation below.
What did you think of this webinar?
Please take a few minutes to complete our online survey. Your feedback will help shape future webinar series!
Zachary H. Seeskin is a Senior Statistician with NORC at the University of Chicago, where he works on sample design, estimation, and data analysis for government and public interest surveys. Seeskin’s research examines benefits and challenges of integrating data from multiple sources for evidence-building, including work published in Statistical Journal of the IAOS and International Journal of Population Data Science.
Seeskin and colleagues are developing tools to assist researchers with evaluating quality of state and local administrative data sources in work for the Family Self-Sufficiency Data Center. He further led a review of uses of Big Data sources for health policy research for the Assistant Secretary for Planning and Evaluation at the Department of Health and Human Services. Seeskin’s expertise and experience includes imputation, adaptive design, and total survey error estimation. He earned his Ph.D. in Statistics from Northwestern University in 2016, where he served as a U.S. Census Bureau Dissertation Fellow.
Kiegan Rice is a Statistician with NORC at the University of Chicago, where she specializes in data visualization, interactive data applications, and reproducible research. Her work focuses on effective communication of research findings through visualization as well as data exploration and assessment through visual analysis. Rice earned her Ph.D. in Statistics from Iowa State University in 2020, where she focused on statistical and computational reproducibility in applied research.