Power of Population Data Science Webinar - Data File Orientation Toolkit: Gaining Insight Into Administrative Data Quality
All sessions will be delivered live and online via the Gotowebinar system.
Can’t attend the live session? This presentation will be recorded and posted on the PopData's YouTube channel and the International Journal of Population Data Science (IJPDS) website for future reference. We recommend you register for the presentations of your choice so we can send you a link to the latest recorded sessions as they are available.
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 introducing Family Self-Sufficiency Data Center’s Data File Orientation Toolkit as a resource providing a framework and code for readily enabling data quality evaluation of administrative data sources. This presentation will demonstrate how to apply to the toolkit with some examples based on Canadian Epidemiological Data from the COVID-19 Outbreak. I will show how to use the toolkit to explore a data file and examine 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.
The current version of the toolkit is available at https://chapinhall.github.io/FSSDC/data-file-orientation-toolkit/
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.