80th Annual Idea Groups

AAPOR Idea Groups

After a popular first year, we are pleased to offer Idea Groups at the Annual Conference again! This innovative pre-conference program offers a unique opportunity for participants to engage in intimate, focused networking sessions through guided discussions.

Taking place from 2:00 pm – 5:00 pm on Tuesday, just before the official start of the conference on Wednesday, Idea Groups provide a platform for attendees to convene in smaller, more specialized gatherings. These gatherings aim to foster dynamic conversations around shared topics and questions that resonate within our AAPOR community.

The primary objective of Idea Groups is to create a conducive environment for informal exchanges, enabling members to delve deeper into pertinent issues and explore collaborative solutions. Whether seeking insights, sharing experiences, or simply connecting with like-minded peers, Idea Groups offer a space tailored to your professional interests.

We encourage all attendees to take advantage of this valuable opportunity to enhance your conference experience and forge meaningful connections within our vibrant AAPOR community. Spots are limited and you can register for one during the conference registration process.

If you have any questions, please email Ryan Green, Education Manager.

AI and Other Advancements in Qualitative Research: Considerations for QUALPOR and AAPOR

Organizer: Darby Steiger

Qualitative research methods have continued to rapidly evolve since the explosion of generative AI over the past few years. In last year’s inaugural Idea Group on the Future of Qualitative Research at the 79 Annual AAPOR Conference, a group of 21 researchers across diverse sectors convened at AAPOR to discuss the ways in which AI tools have the potential to help us become more efficient in all phases of qualitative research. We also discussed our concerns about bias and representation, ethics and consent, and security risks related to using AI tools that may not align with our needs and obligations as social science researchers. The outcome of this session was a collaboratively-created list of over 40 specific ways in which generative AI could support the qualitative research process, along with 11 key areas of concern to keep in mind.

There are three main questions that will be posed in this proposed follow-up to last year’s Idea Group:

  • The first question is to ask AAPOR researchers to share learnings on how our various organizations have been considering, testing, or implementing AI tools since we last met and any challenges or roadblocks we have experienced.
  • A second question is to ask about any other advancements we have been making in the way we design, conduct, analyze and report on qualitative research that are not related to AI.
  • Finally, the third question is to ask and explore considerations and implications of these new methodologies for QUALPOR and AAPOR. Specifically, we will explore the potential for QUALPOR to create a set of guidelines, best practices, or standards for the use of AI in qualitative research, and what those could be.

Teaching America’s Youth about Public Opinion Polling: What Is AAPOR’s Role in (Re)Building Democracy/Democratic Institutions through K-12 Education?

Organizer: Robyn Rapoport 

The proposed idea group will consist of representatives from the AAPOR community (including those involved in both conducting public opinion research and public opinion scholarship) and K-12 education to discuss what is needed to design, disseminate, and deliver effective classroom materials to educate America’s youth about the public opinion polling process and the role of public opinion research within a democracy. Building on Jamieson et al.’s (2023) call for the creation of K-12 education materials as part of a broader effort to protect the integrity of survey research, the University of Pennsylvania’s Leonore Annenberg Institute for Civics, in coordination with industry partner SSRS, is convening experts in the polling and education sectors to consider the following questions:

  1. how do we define the democratic function of polling,
  2. how can the polling process be deconstructed into digested segments from which to build lesson plans,
  3. which polling topics are best situated in civics versus STEM course offerings,
  4. how can these materials be seamlessly integrated into existing courses,
  5. what specific education goals can be met by these educational products,
  6. how do we educate teachers about polling and supplying teacher training for the proposed materials and
  7. how can these efforts be evaluated.

Goals for the idea group also include identifying how to facilitate meaningful collaboration between AAPOR and K-12 education leaders, securing a baseline set of commitments to continue work on the project, and mapping out a work calendar for the next 12 months. Next steps may also involve leveraging learnings from the course for journalists developed through the AAPOR Education Committee.

Reference: Jamieson, K. H., Lupia, A., Amaya, A., Brady, H. E., Bautista, R., Clinton, J. D., … & McNutt, M. K. (2023). Protecting the integrity of survey research. PNAS nexus, 2(3), pgad049. https://doi.org/10.1093/pnasnexus/pgad049

Using Multiple Data Sources for AI Alignment: Bridging Survey Research and Machine Learning

Organizer: Frauke Kreuter

AI and machine learning (ML) researchers are constantly seeking high-quality benchmark data to evaluate and refine their models. For specific skills, such benchmarks often stem from standardized tests or custom-built datasets. However, when it comes to attitudes, behaviors, and living conditions, such benchmarks are scarce. Few survey datasets have been adapted for use in the ML/AI community. One notable exception is the American Community Survey, which has been made available as the “Folktables” dataset. This idea group aims to bring together survey researchers and computer scientists to explore how existing and future human data can enhance the development and alignment of large language models (LLMs). These data could serve not only as benchmarks but also as supplementary sources in the response generation process. The panel will focus on five key questions to ignite interdisciplinary dialogue:

  • Benchmarking: Which role can upcoming surveys place in holding out new data, based on events since LLM models were trained. Doing so will help test a model’s true generalization, and also see if a model can continuously be updated over time.
  • Modeling Success with Biased Data: Given that only a handful of surveys or human data sources are of exceptionally high quality—offering strong representation and measurement—how much can an LLM effectively learn from biased datasets to approximate outcomes similar to high-quality data? What are the risks and trade-offs?
  • The Role of Sparse, High-Quality Data: How much sparse, high-quality data is needed for successful model fine-tuning or re-training? This mirrors the challenges faced in survey methodology, where there is a growing demand for robust benchmarks to calibrate adjustments. What lessons can the two fields share? Likewise holding out data in the benchmark for evaluation specific sub-groups, particularly those that have relatively few survey responses.
  • Trend Modeling with Minimal External Data: How can LLMs be guided to model trends accurately with limited external data? This question opens up opportunities to discuss parallels with small area estimation techniques in survey research, offering a fertile ground for cross-pollination of ideas.
  • Preparing Data for Benchmarking and Alignment: What steps are needed to transform existing and forthcoming datasets into effective benchmarks? How can we ensure these benchmarks are useful for improving model alignment and performance across diverse applications?

This panel — featuring experts from machine learning, theoretical computer science, statistics, psychometrics, and survey methodology — promises to spark a vibrant discussion at the intersection of survey research and machine learning. By tackling these pressing questions, the group aims to lay the groundwork for more robust, human-aligned AI systems, leveraging the untapped potential of diverse data sources.

Bridging Insights from Survey Methods and Natural Language Processing to Improve Both Fields

Organizer: Stephanie Eckman

Large Language Models (LLMs) are driving changes in how we conduct surveys, but survey researchers can also contribute to the development of these models. The Natural Language Processing (NLP) community evaluates LLMs with human-generated data, often collected with survey-like instruments. Insights from survey methods research into how to collect high quality, ethical data and assess the quality of existing data can make AI/ML models more accurate and more efficient. This Idea Group will explore opportunities for joint research on:

  1. High-Quality Data: How to obtain/what is its impact? Data-centric NLP/AI, statistical and social science theory informed data collection.
  2. Trustworthy and Reliable LLM Evaluation: How to reliably evaluate NLP/LLMs to increase trust? Protocols for human evaluation of LLMs and evaluation involving human subjects, ethical considerations, hybrid human-LLM evaluation, trust, actionable evaluation protocols and interpretability (behavioral testing, mechanistic interpretability), etc.
  3. Training and alignment of LLMs: Pre-training and fine-tuning techniques and data to ensure models represent diverse opinions and personas.

Bridging the Data Gap: Enhancing Data Research in Puerto Rico

Organizer: HISP-AAPOR

Puerto Rico, a U.S. territory with over 3 million residents, faces unique challenges that significantly impact data collection efforts. Limited self-governance, unequal access to federal programs, and complex legal hurdles arising from its territorial status create significant obstacles. High poverty rates, exacerbated by the devastating impact of Hurricane Maria, and a persistent outflow of residents further complicate the social and economic landscape. These challenges hinder data collection by creating logistical barriers, limiting access to resources, and impacting the representativeness and reliability of collected data.

This HISP-AAPOR Idea Group will convene leading and emerging experts to discuss these challenges and explore innovative solutions to enhance data research in Puerto Rico. Leading Questions:

  • How do Puerto Rico’s limited economic sovereignty and its political status as a U.S. territory impact its ability to fund and conduct independent research, including its capacity to collect high-quality data?
  • How does Puerto Rico’s relationship with the continental U.S. influence research agendas and the types of data collected, and how can we ensure that research priorities reflect the unique needs and concerns of the Puerto Rican population?
  • What are the most pressing challenges in conducting high-quality surveys of the Puerto Rican population, and how can innovative methodologies and technologies be employed to overcome these challenges and ensure representative samples?
  • How can we build research capacity within Puerto Rico, including training local researchers, fostering collaborations with mainland institutions, and building sustainable data infrastructure, while ensuring that the benefits of such collaborations accrue to the island and its residents?
  • How can we ensure that data collection efforts are resilient and can effectively capture the impacts of natural disasters like Hurricane Maria on the Puerto Rican population?

What can survey methodology / AI learn from AI / survey methodology?

Organizers: Stephanie Eckman, University of Maryland & Gina Walejko, Google

AI is a growing method for answering various research questions. Yet, most AI/ML researchers are not trained in data collection or survey methods. Similarly, few survey methodologists have explored the opportunities that AI and ML may offer the field of survey methodology.

In this idea group we will:

  1. Discuss how ML, AI, genAI, and other advanced technologies might change the way we approach survey design, administration, and analysis; and

  2. Discuss how survey methodology can improve the quality of training data, feedback data, generativeAI workflows, and machine model performance.

To answer the first question, we will discuss:

  • How might AI & ML disrupt the way we currently design, administer, and analyze surveys?

  • What AI applications are on the horizon that will be applied to the current way surveys and public opinion research are done? And, wIll any overcome current limitations associated with surveys?

  • What risks exist?

To answer the second question, we will discuss:

  • How might elements of the cognitive survey response process also apply to the process of labeling of training data and subsequently affect training data quality? For example, which cognitive biases are likely to impact model labeling?

  • What sampling strategies might make machine learning models more fair and more efficient? What sampling strategies should people apply when training their machine learning models?

  • How do we attract diverse labellers?

  • What have we learned from coding open-ended survey questions that can be applied to labeling tasks?

Based on both discussions, we will also discuss:

  • What new skills, education, and synergies that may be beneficial to survey and public opinion researchers and practitioners?

  • What training could our community offer to computer scientists and others engaged in AI / ML, and how do we get computer scientists interested?