Joint Statistical Meeting 2024
NORC at the University of Chicago is pleased to participate in the Joint Statistical Meetings (JSM) 2024, one of the world's largest gatherings for statistics and data science professionals. This important event brings together statisticians, data scientists, and researchers from academia, government, and industry to share research, engage in professional development, and foster collaboration across the field.
Several NORC experts will be attending JSM 2024, contributing to panel discussions, presenting research in poster sessions, and participating in committee meetings. We look forward to engaging with colleagues from various sectors, exploring the latest trends and innovations in statistics and data science, and learning about emerging methodologies and their real-world applications.
We invite you to connect with our team in Portland to learn about our work and discuss potential collaborations. JSM 2024 offers a valuable opportunity to engage with the broader statistical community, gain exposure to new ideas, and contribute to advancing the field. We hope to see you there and participate in the enriching discussions that make this event so beneficial for all attendees.
NORC Presentations at JSM
View the full schedule and featured events on the official conference program online.
Sunday, August 4
- Susan Paddock: From Statistician to Institutional Leader: The Risks & Rewards of Making the Leap
- Don Jang: Networking Like a Pro: a Guided Networking Session
- Kiegan Rice: Measuring viewer engagement with data visualization images in a probability-based survey panel
- Chrys Tadler: Adapting Geographical Sampling Unit Size and Structure for a Changing Survey Landscape
- Brian Wells: Exploring Mode Effect Adjustment Approaches for a Web and Face-to-face Survey
- Zach Seeskin: The Nation's Data at Risk: A First Assessment of the Health of the Federal Statistical Agencies
Monday, August 5
- Jay Breidt: Weight Smoothing via Design Modeling in Complex Surveys
- Frank Rojas: Causal Inference Consulting: Finding an Alternative when Things go Wrong.
- Michael Yang: Evaluating Alternative Estimation Methods Using Combined Probability and Nonprobability Samples
Tuesday, August 6
- Taylor Wing: User Interpretation of Data Visualization Contents Across Chart Types
- Noah Bassel: The Use of Big Data-Based Model Prediction for Stratification of Household Addresses
- Brandon Sepulvado: Detecting and Mitigating Algorithmic Bias in Online Misinformation
- Stas Kolenikov: Survey Weight Calibration in R: Workflows and Pitfalls
- Adrian Dias: Unique Challenges of Weighting Calibration and Impact on COVID-19 Vaccine Hesitancy Outcomes
- Jaclyn Siegel: Intersections Between Body Size, Stigma, and Well-being: Limitations of BMI as a Proxy for Health and Wellbeing
Wednesday, August 7
- Yuhei Koshino: Analysis of Total Survey Error in the 2022 National Immunization Survey-Child
- Martha McRoy: Testing Whether Text and Email Contacts Improve Response in a Large ABS Mixed-Mode Study
- Abby Smith: Evaluating the Impact of Entity Resolution on Observed Social Network Structure
- Carolina Franco: Towards Developing Best Practices for Using Small Area Estimation (SAE) for Diversity, Racial Equity, and Inequality (DREI) Research
- Elizabeth Allen: Integration of Immunization Information Systems for Efficiencies in the National Immunization Survey
Thursday, August 8
- Nick Davis: Using State-Level Medicaid Claims Data to Enhance Survey Estimation
- Chien-Min Huang: A Tree-based Dual-frame Estimation Approach for Combining Probability and Non-probability Samples