Track List with Example Topics

Integrated Program Tracks: Rather than having LLM/AI as a standalone track as we have done the past couple of years, AAPOR is integrating AI, machine learning, and new automated methods across the traditional conference tracks wherever they naturally fit. The goal isn’t to look at AI for its own sake, but to see how it’s used to answer specific research questions.

• Attitudes and Opinions
  Example Topics: public attitudes toward AI; responsible AI governance; emerging, trending, or changing attitudes on key social and civil rights issues; trust in health information; public opinion on health and aging; communicating survey findings credibly; public trust in official statistics

• Data Collection Methods, Modes, and Mixing Modes
  Example Topics: innovations in contact strategies; mode effects in mixed-mode survey designs (including traditional modes vs. AI modes); AI-assisted standardized interviewing (telephone, video, web); evaluating data quality and respondent cooperation with AI interviewers; device effects (mobile vs. desktop) on survey completion and data quality; mode-specific nonresponse and measurement error; collecting health measures and biomarker data

• Data Science, Machine Learning, Neural Networks, and Big Data
  Example Topics: applying supervised and unsupervised machine learning in public opinion research; detecting and mitigating total survey error in algorithmic models; integrating big data and/or administrative or transactional data with traditional survey data; designing, evaluating, and validating predictive models; evaluating algorithmic fairness, transparency, and bias mitigation

• Elections, Politics, and Media
  Example Topics: election-day and pre-election polling methodology; drivers of vote preference and partisanship; media and elite cues shaping political opinion; polling accuracy and public confidence in polls; global and cross-national political opinion

• Field Operations, Logistics, and Costs
  Example Topics: forecasting and managing survey costs; innovations in field staff training, supervision, and quality control; reducing panel attrition; optimizing field operations through automation and new technologies; automating field operations and logistics; physical measures and biomeasures

• Market Research
  Example Topics: agile and continuous tracking research designs for brand and ad performance; advances in market research methods or analysis (e.g., maxdiff, coinjoint, key drivers); in-home use tests; estimating market/mind share; visualizing findings and communicating insights for C-level presentations; agentic AI experience agents; entrepreneurship, independent consulting, and career transitions across the public, private, and vendor sectors; best practices for training and onboarding new market researchers

• Multicultural, Multilingual, and Multinational Research
  Example Topics: cross-national and multilingual measurement and comparability; integrating AI into multilingual survey design, translation, or analysis; advancing international standards for household and individual surveys; substantive findings and methodological issues in 3MC survey research

• Probability and Nonprobability Samples, Frames, and Coverage Errors
  Example Topics: sampling frames; sampling techniques; comparing probability and nonprobability sampling approaches; fraud and self-selection bias in nonprobability samples; innovations in address-based sampling frames; network-based and respondent-driven sampling; improving online panel quality; hybrid sampling designs for small-area population surveys; coverage and sampling error

• Qualitative Research and Text Analytics
  Example Topics: traditional and AI-assisted qualitative interviewing; qualitative data analysis (e.g., thematic analysis, content analysis, grounded theory); text analytics (human, NLP and ML approaches to coding and analyzing open-ended responses, sentiment and topic modeling, and analysis of social media and other unstructured text sources); methodological considerations for qualitative research including quality, bias, privacy, transparency; measurement error across human- and AI-facilitated qualitative research approaches.

• Questionnaire Design, Evaluation, and Pretesting
  Example Topics: questionnaire design experiments; response order, scale design, and question format effects; unmoderated and moderated cognitive interviewing and usability testing innovations; pretesting at scale; using LLMs and ML to draft, evaluate, or pretest survey questions

• Research in Practice
  Example Topics: practical solutions to persistent survey research challenges; navigating federal data gaps and workarounds; the evolving survey research profession, new tools, and techniques; data visualization and communicating results to stakeholders; building the talent pipeline for public opinion research

• Response Rates and Nonresponse Error
  Example Topics: strategies for increasing response rates; predictive modeling for nonresponse; measuring or reducing nonresponse error; nonresponse-related paradata; adaptive and responsive design; incentive experiments; differential response patterns among diverse communities; consent and data linkage decisions

• Statistical Analysis Methods, Weighting, and Estimation
  Example Topics: advances in weighting, calibration, and estimation; imputation; small-area estimation; innovative methods for complex survey designs; multi-level regression and post-stratification; variance; constructing and validationg survey weights