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The Midwest Association for Public Opinion Research (MAPOR) invites eligible students to enter the association’s MAPOR Fellows Student Paper Competition. Two winning papers, the Doris A. Graber Award in Public Opinion, and the Allan L. McCutcheon Award in Survey Research Methodology, will receive $500 and one free conference registration. The winning papers also will be accepted for presentation during a session at the May 2026 AAPOR conference.

2025 Student Paper Competition

DEADLINE: September 30, 2025.

To be eligible for the award, students must submit a full paper for review to by the date above.

a) Criteria: The topic of the paper must fall under one or both of MAPOR’s general areas of scholarship, which are (1) public opinion and (2) research methods in public opinion and survey research. When submitting, the author(s) must indicate the topic for which the paper should be considered. The papers need not be quantitative, nor must they report data to qualify for consideration in this competition. Each student may be an author on only one paper submitted to the competition. Papers up to 6,500 words excluding the abstract, tables, figures, and references. The papers will be judged based on the quality of research design, originality, significance, organization, and presentation.

b) Eligibility: To be eligible to enter the student paper competition, students :

1. Must be enrolled in a graduate or undergraduate program at the time of submission OR received a graduate or undergraduate degree in Winter/Spring/Summer of the award year. Students need not be members of MAPOR but are expected to present at MAPOR of the award year. 

• Cannot have already won a MAPOR Student Paper Award as an author or co-author.

• Cannot submit the same paper over multiple years.

• Papers submitted to this competition should be primarily students’ work, although student author(s) are encouraged to seek advice from a faculty mentor or mentors. This does not preclude recognizing the faculty mentor as a co-author on a later paper that is based on or incorporates some of the work in the student paper. In most cases, the student would be the first author on such a later publication. In cases where faculty mentor(s) are co-authors, in addition to completing the faculty form (see below), a letter must be provided by the faculty mentor or mentors affirming that the student wrote the paper submitted and describing the role(s) the student(s) and faculty played on the paper following Contributors Roles Taxonomy (CRediT) guidelines found here.

c) Award: Each award winner will receive $500 and one free MAPOR conference registration to attend the 2025 MAPOR conference and present their paper.  Additionally, the author(s) of the winning paper will be expected to present their research with a guaranteed spot on the Conference Program at the 81st Annual AAPOR National Conference (2026). 

d) Award Committee: The winning paper and honorable mentions (if applicable) will be selected by a review committee composed of last year’s MAPOR fellow, previous MAPOR fellows, and invited subject-matter reviewers who are MAPOR members.

e) Submission Procedure:

1. Submit an abstract to the MAPOR conference at mapor.org. In addition to a title and abstract, you will be asked to provide all authors' names, institutional affiliations, and email addresses. Students must provide one or more faculty mentor’s name and e-mail address when submitting their abstract. Abstracts can be submitted until _____.

2. Students who have been accepted for the 2025 conference can have their papers considered for the MAPOR Student Paper Competition.  Here are the requirements:

• Follow AAPOR Code of Professional Ethics & Practices and make sure this information is included in the appropriate places in the manuscript. (Please pay particular attention to the disclosure elements in Section III).
• A blinded version of the manuscript must be submitted, wherein all identifiable information about the author(s) and faculty members, acknowledgments, funding information, and other identifiable information is removed or redacted from the manuscript.
• The blinded manuscript must contain the abstract, the body of the paper, all references, all tables and figures, and any appendices.
• If possible, limit self-citations, and, if using, include them in the third person.
• A separate title page containing all author information, including name(s), University affiliation, acknowledgments, funding information, contact information, word count, and the title of the paper.
• Submit all documents in either Word or PDF format.
• Provide the contact information for a faculty mentor/co-author. If you do not have a faculty mentor, please choose a faculty mentor who will review and attest that the paper submission to ensure it meets professional standards of readability, grammar, and so forth. This ensures that the reviewers can attend to the substance of the paper.
• The faculty mentor that the student identifies will also be sent an email asking them to complete an online form which must be completed for a student to be eligible to win the competition. This form will ask them to:
  • List the name of the student entering the competition;
  • Confirm that the submission meets professional standards; and
  • Briefly describe the student’s or students’ role and contributions to the paper following theContributors Roles Taxonomy (CRediT) guidelines found here.

• A member of MAPOR’s executive board will reach out to all students after the abstract submission deadline to confirm details for paper submission to the competition.

• Full papers must be submitted to MAPOR by September 30^th, 2025, by 11:59 PM Central Time.

Winners will be announced at the Friday Awards Session at the MAPOR conference.

It is possible that in any given year, the committee may determine that no paper meets the threshold for granting an award.

Questions may be addressed to either Mary Losch (mary.losch@uni.edu) or Curtiss Engstrom (cwengstr@umich.edu).

Political Polling Insights for Survey Professionals: Margins, Metrics, and Methodology

Polling 101: The Who, What, Why, and How of Modern Political Polling?

Friday, April 11th, 1-2pm CT

In this webinar, we will cover the basics of modern political polling. We’ll discuss the types of polls that are often conducted, the methods and modes that are often used, and the unique challenges and opportunities that face the polling industry. We’ll situate political polling in the total survey error framework, assess the overall accuracy of the polling industry in recent years, and discuss nonresponse bias and changes people in the industry have made to address it.

Joy Wilke is the Polling Director at Blue Labs, a progressive data and analytics firm. At Blue Labs, Joy’s team is responsible for survey data collection across the organization, where they work on everything from questionnaire design, sampling, weighting, programming, and budgeting. She’s worked in polling for 13 years, and has previously held polling positions at Civis Analytics, the Democratic Congressional Campaign Committee, and the Gallup Organization. Joy has her Master’s in survey methodology from the University of Michigan, and her PhD in political science from UCLA.

New Measures of Selection Bias for Pre-Election Polling

Friday, April 25th, 12-1 CT

Recent developments in survey statistics have yielded simple, novel measures of the non-ignorable selection bias in estimates of means, proportions, and regression coefficients that may arise due to deviations from ignorable sample selection, where these deviations might be introduced by the sampling mechanism (e.g., non-probability sampling) or survey nonresponse. This webinar will review the computation of these indicators, the data required to compute them, software tools for computing them, and examples of their use and interpretation based on real survey data. An illustration of the use of these measures to assess the selection bias in pre-election polls conducted for the 2020 presidential election will be presented, followed by discussion of the results and suggestions for future research.

Brady T. West is a Research Professor in the Survey Methodology Program, located within the Survey Research Center at the Institute for Social Research on the University of Michigan-Ann Arbor (U-M) campus. He earned his PhD from the Michigan Program in Survey and Data Science in 2011. Before that, he received an MA in Applied Statistics from the U-M Statistics Department in 2002, being recognized as an Outstanding First-year Applied Masters student, and a BS in Statistics with Highest Honors and Highest Distinction from the U-M Statistics Department in 2001. His current research interests include the implications of measurement error in auxiliary variables and survey paradata for survey estimation, selection bias in surveys, responsive/adaptive survey design, interviewer effects, and multilevel regression models for clustered and longitudinal data. He is the lead author of a book comparing different statistical software packages in terms of their mixed-effects modeling procedures (Linear Mixed Models: A Practical Guide using Statistical Software, Third Edition, Chapman Hall/CRC Press, 2022), and a second book entitled Applied Survey Data Analysis (with Steven Heeringa and Pat Berglund), the third edition of which will be published by CRC Press in April 2025. He was elected as a Fellow of the American Statistical Association in 2022.

Webinar 3: Election Polling in the “Blue Wall”: Lessons from Michigan and Wisconsin

Friday, May 2nd 12-1pm CT

Panelists: Emily Swanson (The Associated Press), Charles Franklin (Marquette University) & Barry Burden (University of Wisconsin-Madison)
Moderator: Erik Nisbet, Northwestern University

A moderated panel of academic and industry experts will discuss polling performance in key Midwestern states, examining the forces that influenced outcomes and how polling methodology is evolving in these politically pivotal regions.

	Emily Swanson is director of public opinion research at the Associated Press, where she directs AP's polling team and election night decision desk. The decision desk analyzes vote returns, historical data, and the results of AP VoteCast, AP’s pioneering election research survey, to determine when AP officially calls the winner in elections across the country and explains to the world how we know. Swanson played a key part in the development of AP VoteCast. She also oversees polls conducted by the AP-NORC Center for Public Affairs Research.
	Charles Franklin (Ph.D, Political Science)  is Professor of Law and Public Policy at Marquette Law School, where he directed the Marquette Law School Poll, ranked 2nd of over 500 pollsters nationally by Nate Silver.  Prior to Marquette Law School was Professor of Political Science at the University of Wisconsin Madison for 22 years before leaving to join the Marquette Law School in 2012. He is past president of the Society for Political Methodology and an elected Fellow of the Society. From 2002 to 2020 he was a member of the ABC News election night Decision Desk.
	Barry Burden (Ph.D., Political Science) is Professor of Political Science, Director of the Elections Research Center, and the Lyons Family Chair in Electoral Politics at the University of Wisconsin-Madison. Burden's research and teaching focus on U.S. elections, political parties, public opinion, representation, and the U.S. Congress. His recent research examines aspects of election administration and voter participation. Burden earned his Ph.D. at The Ohio State University and was a faculty member at Harvard University before joining UW-Madison in 2006.

Generative AI 101: How Survey Researchers Can Apply AI

November 8, 2024 at 11:30am CT

Claire Kelley and Sarah Kelley

Child Trends

How can generative AI be used to support survey research? In this introductory webinar we will provide a primer on how generative AI works and how it can be used for survey research. This course will cover a (math free) introduction to how generative AI models are built, and introduce core concepts such as API's,  transformers, prompt engineering, few-shot learning, and fine-tuning. We will provide a brief overview of how generative AI is currently used in survey research with a focus on how learners can apply these concepts to their own research. 

Registration to this webinar gives you access to the live webinar as well as access to the recording for one price. The cost for MAPOR members is $10, $40 for non-members, and FREE for students with a valid .edu email address.

Claire Kelley is a co-program area director of data science and senior data scientist at Child Trends, where she conducts and supports research across all program areas. Her primary research interests focus on the intersection of machine learning and social science, particularly in the domains of health and education. In her work, Claire blends traditional quantitative methods with machine learning and software engineering. Some of her recent projects include using computer vision to assess bias in news articles, co-authoring an open source software package for fitting mixed effects models with complex survey weights, and creating an interactive JavaScript-based data story about algebra enrollment. In addition to writing software and conducting research, Claire is passionate about creating a community of practice around data science for social research. She regularly presents her research and teaches professional development courses at data science and social science conferences including PyData, the American Association for Public Opinion Research, and the American Education Research Association. Prior to joining Child Trends, Claire worked as a data scientist at the American Institutes for Research (AIR) and at Merck. At AIR, Claire worked on a variety of data engineering and data science projects and led the data visualization and reporting working group. At Merck, she worked on e-commerce data science, including developing a parallelized system for generating product recommendations and using time series models for forecasting product demand. She holds a bachelor’s degree in statistics from Yale University and a master’s degree in quantitative methods from Columbia University.

Sarah Kelley is a co-program area director of data science and senior data scientist whose research focuses on applying data science techniques— especially natural language processing, computer vision, and machine learning—to answer questions related to social science and education. She has worked on a diverse set of social science problems, from using social media data to explore public conversations around child abuse in Haiti, to using big data and geospatial statistics to understand drivers of the opioid crisis, to using natural language processing to gain insight into political polarization online. Sarah is particularly excited about the potential of data science methods to augment traditional research methods. She seeks to make her work accessible to general audiences through data visualization. She also hopes to support quantitative researchers by using data science methods to create and integrate rich data sets, providing data access through application programming interfaces, and linking data sets using machine learning methods. Previously, Sarah was a data scientist at the American Institutes for Research, where she led projects focused on applying computer vision algorithms in education contexts, developing large-scale data processing pipelines, and using machine learning techniques to improve predictive modeling. She holds a bachelor’s degree in sociology from Yale University and a master’s degree in data science from the University of California, Berkeley.