I checked 7 public opinion journals on Friday, October 02, 2026 using the Crossref API. For the period September 25 to October 01, I found 10 new paper(s) in 3 journal(s).

Journal of Official Statistics

Beyond the Trade-Off Curve: Multivariate and Advanced Risk-Utility Maps for Evaluating Anonymized Data
Oscar Thees, Roman Müller, Matthias Templ
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Anonymizing microdata requires balancing disclosure risk reduction with the preservation of data utility. Traditional evaluations often rely on single measures or two-dimensional risk-utility (R-U) maps, but real-world assessments involve multiple, often correlated, indicators of both risk and utility—a fundamentally multivariate problem that pairwise comparisons fail to capture both efficiently and completely. We compare six visualization approaches for the simultaneous evaluation of multiple risk and utility measures: heatmaps, dot plots, composite scatterplots, parallel coordinate plots, radial profile charts, and principal component analysis (PCA)-based biplots. We introduce blockwise PCA for composite scatterplots and joint PCA for biplots that simultaneously reveal method performance and measure interrelationships, and apply systematic Pareto-optimal method identification across all approaches where applicable, with dominance assessed in the original composite score space. Our comparison shows that no single approach dominates across all criteria: PCA biplots perform well on the analytical criteria, in particular for revealing how the risk and utility measures relate to one another, while composite scatterplots do so on scalability and applicability and are the only approach that can display the Pareto front. Combining complementary visualizations provides the most complete basis for evaluating the risk-utility trade-off.

Journal of Survey Statistics and Methodology

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.
Model-Based Estimators Using Area-Level Models With Clustered Effects
Xin Wang, Konnor Payne
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Model-based estimators are widely used in small area estimation problems to provide reliable estimates for domains with small sample sizes. When auxiliary information is available only at the area level, area-level models are typically used. We propose a new estimator based on an area-level model with clustered effects. This model allows for heterogeneity in regression coefficients across areas by incorporating clustered coefficients. Pairwise penalties are used to simultaneously identify clusters and estimate parameters. In simulation studies, we compare the performance of the proposed estimator with that of existing estimators. Additionally, we apply the new estimator to the Forest Inventory and Analysis data.
Room for Error: Identifying Question Order Effects in Factual Measures When Meaning or Context Shifts
Daniel Goldstein
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Long-running surveys designed to measure trends face a dilemma of prioritization: making improvements and updates to survey items or maintaining consistent instrumentation across survey waves. This includes the order of questions, as researchers have shown that earlier questions in a survey may affect respondents’ answers to later questions, though inconsistent question order across repeated surveys is underdiscussed as an issue for factual items. Primarily theorized to function through the cognitive response process whereby the prior question(s) frames or provides a context for the latter question(s), this phenomenon is known as question order effects. This article examines an example of a clarification order effect in factual questions where reordering the questions reduces confusion and leads to more valid responses. Using data from three surveys conducted in New York City, we examine the impact of question order on estimates of housing unit size. In two surveys where total residential rooms were asked about before bedrooms, paradata indicated increased respondent confusion relative to asking about bedrooms first. In two surveys where the items either had their order reversed or were reformatted to appear closer together, respondents reported more total rooms on average. Qualitative interviewer feedback suggested that when confronted with a question about rooms without other context, respondents often gave the number of bedrooms in their home. They frequently corrected their response after being asked about bedrooms, though not universally. We suggest that this order effect arose because contemporary respondents think more readily about their unit size in terms of bedrooms than rooms. Our analysis motivates our recommendation that questionnaire designers consider question order alignment with background respondent salience as another tool beyond definitions to help comprehension. Lessons indicate that regular and frequent review of survey items, order, and formatting for potential updates can smooth discontinuities and both improve measurement and maintain continuity.
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.
ASSESSING THE IMPACT OF VIDEO INTERVIEWING ON MEASUREMENT QUALITY: EVIDENCE FROM AN EXPERIMENTAL STUDY ON MODE EFFECTS
Marc Asensio, Matt Brown, Richard Silverwood, Tim Hanson, Gabriele Durrant
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Amid declining response rates and rising survey costs, identifying reliable and cost-effective data collection methods is crucial. During the COVID-19 pandemic, major social surveys began to adopt video interviewing as an alternative to in-person data collection. However, its impact on measurement quality remains underexplored. This study addresses this evident gap by comparing mode effects between video, web, and in-person survey modes, using data from the first large-scale experiment where 1,692 respondents aged 20–40 from a nonprobability convenience sample were randomly assigned to one of the three modes. We focus on two key measurement quality indicators: item nonresponse and response distribution. Our results show that video interviews yielded slightly lower item nonresponse levels than in-person interviews, albeit these differences are almost negligible. While measurement differences in responses to survey questions between the interviewer-administered modes were minimal, differences between video and web responses were more apparent, particularly on sensitive items like mental wellbeing and financial difficulties. Our findings suggest that video interviews are comparable to in-person surveys, but they may also suffer from the usual social desirability biases associated to the interviewer presence. Overall, these results are promising and suggest that video interviewing could serve as a valuable complement or alternative to traditional face-to-face surveys.

Politics, Groups, and Identities

Students as knowledge brokers: voter information sharing and the political consequences of informing students of color and first-generation students on electoral politics
Maricruz A. Osorio, Stephanie L. DeMora, Melissa Michelson, Kesicia A. Dickinson, Jasmine C. Jackson, Jazmin Jimenez
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Navigating state fragility: gender, insecurity, and informal networks in the Haitian judiciary
Marianne Tøraasen
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The rise of the Republican Party support in democratic strongholds along the U.S.-Mexico border: the political efficacy of conservatives in the Rio Grande Valley, Texas
Mi-son Kim, Natasha Altema McNeely, Dongkyu Kim
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