I checked 7 public opinion journals on Tuesday, May 05, 2026 using the Crossref API. For the period April 28 to May 04, I found 8 new paper(s) in 5 journal(s).

Journal of Elections, Public Opinion and Parties

Why survey turnout data misleads: probing, selection bias, and misinterpreted political participation
Kasper M. Hansen
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The conditional value of legislative effectiveness
Elizabeth N. Simas
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Journal of Official Statistics

Assessing the Heterogeneity in Mode Effects on Data Quality, Response Distributions, and Future Participation Across Sociodemographic Subgroups in a Mixed-Mode Panel Study
Heather M. Schroeder, Mary Beth Ofstedal, Brady T. West
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Introducing a web option in interviewer-administered surveys could increase response rates and reduce costs. However, this requires careful assessment of the effects of mixed-mode designs on data quality and key measures, especially across important sociodemographic subgroups. In 2018 and 2020, the Health and Retirement Study (HRS) experimentally introduced web in a sequential mixed-mode design for panelists assigned to the telephone mode. Initial analyses found a limited number of mode effects on key outcome distributions and data quality measures. This paper extends this initial analysis by assessing possible heterogeneity in these effects among sociodemographic subgroups defined by race/ethnicity, sex, and others. We interact mode with each sociodemographic indicator in statistical models for each outcome. Overall, we found limited evidence of heterogeneity in the mode effects, with 3% of the 204 interaction terms we tested emerging as significant. For example, previous work showed that more household roster changes are reported in the web-first group, and we found that this was more pronounced for females and those with some college education. Although some heterogeneity in mode effects was observed across subgroups, the effects were generally too small to cause data quality concerns. We conclude with a discussion of broader considerations for survey researchers.
Comparison of Small Area Procedures Based on Gamma Distributions with Extension to Informative Sampling
Yanghyeon Cho, Emily Berg
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The gamma distribution is a useful model for small area prediction of a skewed response variable. We study the use of the gamma distribution for small area prediction. We emphasize a model, called the gamma-gamma model, in which the area random effects have gamma distributions. We compare this model to a generalized linear mixed model. Each of these two models has been proposed independently in the literature, but the two models have not yet been formally compared. We evaluate the properties of two mean square error estimators for the gamma-gamma model, both of which incorporate corrections for the bias of the estimator of the leading term. Finally, we extend the gamma-gamma model to informative sampling. We conduct thorough simulation studies to assess the properties of the alternative predictors. We apply the proposed methods to data from an agricultural survey.

Journal of Survey Statistics and Methodology

SCREEN RESOLUTION AND QR CODES IN PUSH-TO-WEB SURVEYS: A QUICK RESPONSE TO SURVEY BREAKOFF?
Katharina Pfaff, Christina Walcherberger
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The integration of QR codes (quick response codes) in web surveys has become commonplace, aimed at simplifying survey access for respondents using mobile devices. While previous studies have presented mixed findings regarding the effectiveness of QR codes in enhancing survey participation, this research re-examines their impact on survey breakoff, identifying potential explanatory factors such as the screen resolution of the respondent’s device. Drawing on data from the Digitize! Online Panel Survey, a probability sample of up to 1,228 respondents in Austria, this study uses an exploratory research design to investigate how screen resolution, QR code usage, and frequency of personal computer or mobile device use influence survey breakoff behavior. We find that QR code utilization alone does not significantly predict survey breakoff once other covariates are accounted for. However, the resolution of the device used for the survey emerges as a critical determinant, with higher resolutions associated with lower probability of survey breakoff. The study thus uncovers a nuanced relationship between screen resolution and survey breakoff, particularly noticeable when transitioning from mobile devices likely to be smaller, such as smartphones, to those with larger screens, like tablets. While QR codes offer convenience and accessibility advantages, their implementation requires careful consideration to mitigate potential drawbacks and optimize survey engagement. Overall, this study contributes valuable insights into the evolving landscape of digital survey methodologies, highlighting avenues for future research to enhance data validity and reliability.

Politics, Groups, and Identities

The summer of 2020: racialized framing and how threat is used to oppose social activism
J. Scott Carter, Annie Jones
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Showing up is the hardest part: examining geographical barriers to immigration court access
Miranda E. Sullivan
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Social Science Computer Review

A Bottom-Up Approach for Ecological Inference
Jose M. Pavía, Alberto Penadés
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The ecological inference canonical problem in the social sciences consists of estimating the unobserved internal counts of a global RxC table from the known margins of a set of units. This paper proposes a new, computation-based strategy designed to better exploit the information contained in the unit margins. This approach can be integrated into any ecological inference method that explicitly estimates unit tables and accounts for differences in unit size. We evaluate its performance using as a baseline the fastest ecological inference linear programming method, relying on real electoral data from over 550 datasets where true contingency tables are known. In this extensive assessment, the proposed strategy reduces average global errors by more than 21% relative to the baseline, outperforming it in 95% of cases. It also improves upon nslphom—identified in the literature as the most accurate algorithm for this dataset—reducing average global errors by over 5% and outperforming it in 60% of cases. The versatility of the approach is further illustrated by also integrating it into three more computationally intensive methods, including the two main statistical ecological inference models—ei.MD.bayes and BPF—and nslphom, yielding consistent improvements over their respective baselines in a small set of examples.