This webinar is part of the Power of Population Data Science Series
In the United States, state and local agencies administering government assistance programs have in their administrative data a powerful resource for policy analysis to inform evaluation and guide improvement of their programs. Understanding different aspects of their administrative data quality is critical to guide informed use of such data files for analyses. However, state and local agencies often lack the resources and training for staff to conduct rigorous evaluations of data quality before making the data available to researchers.
This presentation provides an orientation to The Family Self-Sufficiency Data Center’s Data File Orientation Toolkit that provides a framework and code to more readily enable data quality evaluations of such data sources. The toolkit organizes analyses by key 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. In addition, the Family Self-Sufficiency Data Centre incorporates data visualization to draw attention to sets of records or variables that contain outliers or for which quality may be a concern. Principles for more customized data quality analysis that takes into account the particularities of a data file will also be addressed in this presentation.
Watch recorded presentation below.
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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.