Below are links to CIPHER 2026 presentations (in PDF), associated papers, and recordings of presentations if available.
| Presenter | Presentation (PDF) | Link to Recording or Papers |
| UAS Session A: Health, Time Use, and Survey-Based Evidence | ||
| Michael Sobolev | Replicating Health Behavior Research using the Understanding America Study | |
| Italo Lopez Garcia | Job Demands, Time Use, and Cognitive, Mental and Physical Health over the Lifecourse | |
| Nana Xia | Multimodal Prediction of Emotional Health: Linking Fitbit and PROMIS Sleep Data in the American Life in Realtime (ALiR) Study, A Nationally Representative Digital Health Benchmark | |
| Grigory Franguridi | Estimation and inference for panels with unrestricted attrition | Paper |
| UAS Session B | ||
| Kyla Thomas | After the Flames: Longitudinal Insights from the LABarometer Wildfire Study | |
| Jiawen Liao | Adverse Mental Health Impact of 2025 Los Angeles Wildfire Disaster: A Difference-in-Difference Analysis of the Understanding America Study | |
| Lingzi Luo | Recent Adverse Life Events, Mental Health, and Sources of Professional and Non-Professional Support Among U.S. Adults: Findings From the Understanding America Study | NA |
| Wändi Bruine de Bruin | Should we change the term we use for “biodiversity loss”? Evidence from a U.S.-wide terminology experiment | |
| JoNell Strough | Aging and Emotional Resilience to Extreme Weather: Testing Mechanisms Using the Understanding America Study | |
| UAS Session C | ||
| Jeremy Burke | Is Financial Knowledge Really Declining? Randomized Evidence on the Effects of Smartphone Responses | |
| Vikesh Kumar | U.S Households’ Financial Well-being and Crypto Investment Decisions | |
| Anthony Garove | The Effect of Requesting Financial Data Linkage on Survey Participation | |
| Laila Tasneem Bera | A Pervasive Threat: Analyzing UAS Survey Data on Frauds and Scams | |
| Session 1: New Probability Panels and Innovations in Recruitment | ||
| Henning Silber | Creating the M Panel: A New Probability-Based Online Panel of the General US Population | |
| Ellyn Maese | Recruiting Conservative Panel Members: What Works, What Doesn’t, and What’s the Trade-Off | |
| Amie Rapaport | UASTeen: Introducing the UAS’ new Teen Panel | |
| Frank Kelly | Five Dollars and a Click: Field Evidence on Digital Post-Incentives in an Address-Based Sample | |
| Session 2: Novel Applications I | ||
| Laith Alattar | Using the UAS Comprehensive File to Evaluate Social Security Program Knowledge and Communication Preferences | NA |
| Brittany Alexander | Mapping County-Level Religious Diversity: A Comprehensive Bayesian Approach to the 2023 Census of American Religion | |
| Marcin Hitczenko | Using Survey Metadata to Identify Patterns in Satisficing | |
| Kim P. Huynh | Wear am I in your wallet? Cash Holdings of Mobile Payment Users | |
| Kathleen Mullen | Measuring Work Capacity | |
| Session 3: Survey Attitudes, Participation, and Panel Engagement | ||
| Megan A. Hendrich | Early Birds vs. the Full Flock: Representativeness of Probability Samples Across the Field Period | |
| Emilio Rivera | Sustaining Engagement in a Probability-Based Panel: Early Evidence and Panelist Preferences for SMS Survey Reminders | |
| Yongchao Ma | Survey Attitudes and Survey Participation – and Vice Versa | |
| Alexandros Christos Gkotinakos | Opting Out of Politics—and the Panel? Civic Disengagement and Attrition in Probability-based Online Panels | |
| Lunch Keynote | ||
| Trent D. Buskirk | Let’s Not Leave Probability Panels to Chance: Why AI Matters for Their Future Designing Smarter, More Resilient Probability Panels for the AI Era |
Recording |
| Session 4: Representativeness in Probability Panels | ||
| Jared Coopersmith | Evaluating the representativity of KnowledgePanel | |
| Lena Rembser | Who do we not reach with online-only? Insights from a mixed-mode panel | |
| Jon A. Krosnick | Accuracy of 51 samples from non-probability online panels and river samples vs. RDD telephone. A reanalysis of data from the most recent Advertising Research Foundation study | NA |
| Benjamin Phillips | Using knock-to-nudge methods for recruitment to a probability-based online panel: Findings from Australia | |
| Session 5: Monitoring and Improving Data Quality in Probability Panels | ||
| Cristina Tudose | Navigating data quality: Comparative insights from probability-based and opt-in online panels across Europe | NA |
| Mickey Jackson | Who fails data quality checks in probability panels? And what (if anything) should we do about it? | |
| Stephen Raynes | Monitoring Data Quality in Probability-Based Internet Panels | |
| Joris Mulder | From Surveys to Digital Traces: Insights from Data Donation Studies in the Dutch LISS panel | |
| Session 6: Experimental Evidence on Recruitment for Probability Panels | ||
| Cameron McPhee
Kevin Collins |
Using Texting to Pre-Paid Cells to Reach Underrepresented Populations | |
| Ipek Bilgen | Incorporating Text Messaging into Probability-Based Panel Recruitment: Experimental Insights on Response and Retention | |
| Darby Steiger | Will You Open the Envelope? Improving Recruitment Materials for a Probability-Based Panel | |
| Tracy Keirns | Text-to-Web Panel Recruitment: Demographic Differences in Panel Join Rates and Survey Completion Rates | |
| Session 7: Novel Survey Designs, Linkages, and Emerging Data Sources |
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| Jennifer Sinibaldi | Can We Replace Employment Questions with Auxiliary Data from LinkedIn or Resumés? | |
| Joshua Claassen | Going beyond survey self-reports: Processing, enriching, and analyzing digital traces | |
| Nick Bertoni | There’s an app for that – lessons learned from building and implementing a mobile app for KnowledgePanel | |
| Evan W. Sandlin | Using an Online Probability Panel to Collect Residential History | |
| Session 8: Artificial Intelligence | ||
| Nicholas Biddle | Generative AI data donation – Who donates, how to increase it, and why it matters | |
| Ting Yan | Leveraging Artificial Intelligence to Code “Other/Specify” Responses in Surveys | |
| Jan Karem Höhne | Survey data contamination through Large Language Models: Predicting LLM-generated answers to open narrative questions | |
| Andrew Parker | Coding at Scale: Human-LLM Partnerships in Large-Sample Qualitative Research | |
| Session 9: New Tools for Survey Data Access, Integration, Comparison, Analysis, and Visualization | ||
| Andy Peytchev | Multiple Imputation for Combining Probability-Based Web Panel and National Interviewer-Administered Survey Data with Content Overlap | |
| Matthias Schonlau | Snapshot plots: a visual summary of your data | |
| Bas Weerman | Context-Aware AI Search with UAS data and LLMs | |
| Paul Scanlon | Beyond Response Rates: Comparing Data Quality Across Probability and Non-Probability Web Panels Using Measurement Invariance and Text Analysis Approaches | |
| Marco Angrisani | Public Attitudes Toward Artificial Intelligence and Conversational Technologies: Evidence from the Understanding America Study | |
| Lunch Keynote | ||
| Ben Gurga | Virtual Assistants at Social Security | |
| Panel Discussion | ||
| Marcel Das
Trent Buskirk Tom Emery Sebastian Lundmark Brady T. West |
Next Generation Social Surveys: Innovations in Design, Data Linkage & Digital Tools | |
| Session 10: Data Quantity in the Age of AI | ||
| Arundati Dandapani | Synthetic Data: Method or Mirage? AI Governance Trade-Offs in an Unstructured World | |
| Frank Graves | The Polling Paradox: How Survey Methods Confront—and Sometimes Fuel—the Disinformation Crisis | |
| Aleia Clarke Fobia | Future-Ready by Design: Government Data Quality Frameworks in an AI-Augmented World | |
| Session 11: Novel Applications II |
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| Ritika Chaturvedi
Tadeja Gracner |
Describing lifecourse GLP-1/GIP experience in the Understanding America Study, a nationally-representative population health study | |
| Lisa Bondo Andersen | From Cursor to Cognition: Clustering Respondent Styles with Neural Embeddings | |
| Dan Cassino | “Not the Man They Think I Am”: Gender Role Strain as a Predictor of Cognitive Decline | |
| Yiwen Cao | Assessing the Consequences of Caregiving for Individuals’ Well-Being in a Representative Sample: A Longitudinal Analysis Using Survey and Wearable Data | |
To go back to CIPHER 2026 website, click here.