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.
For the Survey of Doctorate Recipients, Seeskin contributes to imputation, adaptive design, and flow processing to produce weighted survey estimates during data collection. Seeskin and his 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 led a literature review on uses of Big Data sources for health policymaking for the Assistant Secretary for Planning and Evaluation at the Department of Health and Human Services. In addition, Seeskin worked on imputation of a longitudinal study evaluating the effectiveness of an anti-smoking campaign.
Seeskin’s research examines questions facing the Federal Statistical System, including assessing administrative data quality, and understanding benefits and challenges of integrating administrative data sources with surveys for official statistics and policymaking. He has measured the benefits from improving accuracy of the U.S. Census for congressional apportionment and allocations of federal funds. Seeskin earned his Ph.D. in Statistics from Northwestern University in 2016, where he served as a U.S. Census Bureau Dissertation Fellow.
His other experiences with statistical modeling include estimating the risk for increased crime due to street light outages for the Chicago Department of Transportation, consulting for medical research at Northwestern, and contributing to economic research as an associate economist at the Federal Reserve Bank of Chicago.