I checked 7 public opinion journals on Saturday, September 26, 2026 using the Crossref API. For the period September 19 to September 25, I found 10 new paper(s) in 3 journal(s).

Journal of Elections, Public Opinion and Parties

AI as a partisan cue: the relational dynamics of AI-assisted campaign messaging in election integrity perceptions
Seulki Lee-Geiller
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Ideological principle or outgroup attitudes? An experimental analysis of cross-pressures, social dominance orientation, and vote choice among white Americans
Levi G. Allen, Spencer C. Lindsay
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Journal of Survey Statistics and Methodology

Conditional Optimal Sampling: Enhancing Unequal-Probability Designs with Auxiliary Information
P GarcĂ­a-Segador, P Miranda
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We address the problem of selecting a fixed-size sample from a finite population when the goal is to reduce the estimation error as much as possible. Our approach builds on the idea of choosing samples that are nearly optimal with respect to the values of the study variable, even though these values are not observed in advance. Instead, we rely on predictions derived from auxiliary information, for example, through statistical or machine-learning models, and construct sampling designs that are optimal for these predicted values. We show that this strategy is remarkably effective: even when the predictions are imperfect, the resulting designs tend to be very close to the unknown optimal design based on the true values. To understand why this happens, we study the geometric structure underlying the space of all feasible sampling designs and use this perspective to develop practical algorithms. Simulation experiments indicate that the proposed methodology achieves noticeably lower estimation error than classical unequal-probability sampling methods commonly used in the literature, including balanced procedures such as cube and classical fixed-size designs like conditional Poisson sampling.
Motivating The Unmotivated? Testing Conditional Lottery Incentives in an Ongoing Panel Survey
Rasmus Patton, Jannes Jacobsen, Jörg Dollmann, Almuth Lietz
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This study examines whether conditional lottery incentives, where the probability of winning increases with continued participation, can motivate panelists to reengage in a randomly sampled, quarterly online survey. Respondents were randomly assigned to one of three groups: a control group (CG) receiving the standard €10 incentive, treatment group 1 (T1) with an additional lottery offering ten Amazon vouchers worth €500, and treatment group 2 (T2) introducing conditionality by linking the probability of winning to participation in successive waves. This design tests whether reengagement depends not only on incentive size but also on respondents’ perceived ability to influence their odds of winning. Results show no statistically significant treatment effects. Neither the high-value lottery nor its conditional variant increased participation. Subgroup analyses reveal no systematic differences across prior participation, education, income, or country of origin. These findings confirm earlier evidence on the limited effectiveness of lotteries in survey research but extend it by testing conditionality as a motivational mechanism. Although conditional incentives strengthen the conceptual link between effort and reward, restoring agency alone appears insufficient to overcome deeper motivational barriers in quarterly online panels.
What Informs Interviewer Question Asking and Probing? Evidence from the Survey of Income and Program Participation
Rodney L Terry, Erica C Yu, Holly Fee
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Interviewers play a crucial role in the outcome of an interview because their behavior during interview interactions can affect data quality and respondent burden. Existing models of interviewer behavior hypothesize a range of reasons why interviewers deviate from scripted protocols, but these models lack an essential piece of the puzzle: the information driving interviewer decision-making. In this article, we propose a model of the information acquired by the interviewer during the interview process, focusing on the interviewer’s early impressions of the physical interview environment and respondent doorstep behavior (also known as interviewer observations) and the interviewer’s knowledge about the respondent, as gained through the asking and recording of responses to survey questions. To evaluate this model, we used several data and paradata sources from wave 1 of the 2014 Survey of Income and Program Participation (SIPP) Panel, including computer audio-recorded interview (CARI) recordings, interviewer observations, and survey responses collected during the household interview. Consistent with previous research, question characteristics were the most powerful predictors in our models of interviewer behavior. However, we did find limited evidence of interviewers using information they acquired during the interview, including interviewer observations and survey response data, to inform deviations in question asking. Future research and interviewer training implications are discussed.
Using Recordings of Survey Interviews and Machine Learning to Evaluate Differences in Interviewer–Respondent Interactions Between Telephone, In-Person, and Video Interviews
Hanyu Sun, Brad Edwards, Ting Yan
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Computer-Assisted Recorded Interviewing captures audio recordings of survey interviews in real time, producing a distinctive form of unstructured paradata—question-level audio files that capture the question–answer sequences between the interviewer and respondent. These recordings offer insights into the survey response process, enabling a deeper understanding of how responses are formed and delivered. This paper explores the use of machine learning to automate the processing of recordings and to compare interviewer–respondent interactions in video interviews with those of in-person and telephone interviews in the production environment of a large national survey of US households. We first applied machine learning algorithms to transcribe the audio recordings and generate indicators describing interviewer–respondent interactions. We then assessed differences across the three modes in two interviewer behaviors indicative of adherence to standardized interviewing protocol (reading questions verbatim and maintaining question meaning) and in three interaction outcome measures indicative of respondent difficulty answering survey questions (number of the respondent’s turns, duration of the respondent’s first turn, and the total duration of turns with speech). We found significant differences in interviewer question reading behaviors and in the duration of the respondent’s first turn across the three modes, but the differences were small. Additionally, the question itself emerged as the primary source of variability across all outcome measures. This paper demonstrates a viable method for using recordings to efficiently assess interviewer–respondent interactions for research purposes.

Politics, Groups, and Identities

Revisiting “Trump’s African Americans”: racial attitudes and vote choice in the 2024 presidential election
Jonathan Knuckey
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Are young populists different from older populists? Investigating the relationship between age and populist attitudes
Daniel Stockemer, Jean Nicolas Bordeleau
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Muslim identity, criticism of Israel, and attitudes toward Jews: a test of the new antisemitism thesis
Jeffrey E. Cohen, Robert Brym
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The politics of care: the effects of caregiving and discrimination on black women’s political engagement
Ayana Best
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