Emily Wiegand is Analytics Manager at Chapin Hall. Her work focuses on the use of administrative data to guide strategic planning and decision making in the nonprofit and public sectors and to create evidence-based policy. She partners with local, state, and federal agencies to assess data quality and availability, build capacity to improve administrative data systems, and facilitate the use of data for research and analysis. Wiegand is the Principal Investigator for an early learning data system assessment, and she leads components of the Family Self-Sufficiency Data Center and TANF Data Innovation projects. In addition to her knowledge of quantitative analysis, Wiegand has extensive experience with data manipulation, database development, and probabilistic record linkage theory and practice. She oversees maintenance and upgrades to Chapin Hall’s Integrated Database on Child and Family Programs in Illinois, which links administrative data on social service receipt, education, criminal and juvenile justice, employment, healthcare, and early childhood programs. Wiegand routinely advises on the design of research using these data sources.
Prior to joining Chapin Hall, Wiegand was a member of the inaugural class of the Eric and Wendy Schmidt Data Science for Social Good Fellowship, where she combined administrative records with survey data to benchmark the effectiveness of an early childhood home visiting program. She previously worked for the United States Department of Health and Human Services Office of the Inspector General on an assessment of plans to improve the quality of federally collected Medicaid data. She also has experience in data analysis, reporting, and visualization in the nonprofit sector, particularly in fundraising.
Wiegand has a Master of Public Policy and a Bachelor of Arts in History, both from University of Chicago.
Master of Public Policy, University of Chicago
Bachelor of Arts in History, University of Chicago
Wiegand, E. (2019, October). Complexities and practical solutions for linking human services datasets. Presentation at the 2019 Committee on National Statistics Data Linkage Day, Washington, DC.
Wiegand, E. (2019, July). TANF agencies’ access to data for research and analyses. Presentation at the 2019 National Association for Welfare Research and Statistics Annual Workshop, New Orleans, LA.
Wiegand, E., Goerge, R., & Gjertson, L. (2019, July). Describing TANF caseloads: Methods and considerations from the Family Self-Sufficiency Data Center. Presentation at the 2019 National Association for Welfare Research and Statistics Annual Workshop, New Orleans, LA.
Wiegand, E. (2018, May). Tools and methods for cross-state knowledge sharing and collaboration in using data. Presentation at the 2018 Research and Evaluation Conference on Self-Sufficiency, Washington, DC.
Wiegand, E. (2016, June). Family Self-Sufficiency Data Center: Tools and methods for enhancing the use of administrative data. Presentation at the 2016 Research and Evaluation Conference on Self-Sufficiency, Washington, DC.
Wiegand, E. (2015, August). State data needs around family self-sufficiency from the Family Self-Sufficiency Data Center. Presentation at the 2015 NAWRS Annual Workshop, Atlanta, GA.
Louis Brownlow Award, 2018 (best practitioner-related article published in Public Administration Review)
Goerge, R. M., & Wiegand, E. R. (2019). Understanding vulnerable families in multiple service systems. RSF: The Russell Sage Foundation Journal of the Social Sciences, 5(2), 86–104.
Allard, S. W., Wiegand, E. R., Schlecht, C., Datta, A. R., Goerge, R. M., & Weigensberg, E. (2018). State agencies’ use of administrative data for improved practice: Needs, challenges, and opportunities. Public Administration Review, 78(2), 240–250.
Thorland, W., Currie, D., Wiegand, E. R., Walsh, J., & Mader, N. (2017). Status of breastfeeding and child immunization outcomes in clients of the Nurse-Family Partnership. Maternal and Child Health Journal, 21(3), 439–445.
Wiegand, E., Goerge, R., & Gjertson, L. (2017). Family Self-Sufficiency Data Center: Creating a data model to analyze TANF caseloads. Washington, DC: Family Self-Sufficiency and Stability Research Consortium.