I checked 7 public opinion journals on Sunday, May 03, 2026 using the Crossref API. For the period April 26 to May 02, I found 5 new paper(s) in 3 journal(s).

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.
Expanding Your Vocabulary: A Framework for Topic Integration in Texts
Roy Gardner, Matthew Martin, Ashley Moran, Zachary Elkins, Andrés Cruz, Guillermo Pérez
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Topic discovery and integration are vital for maintaining vocabularies that categorize textual corpora. Automated approaches are often computationally expensive and lack domain-specific conceptual nuance; manual approaches are costly in terms of time and potential bias. To address this dilemma, we introduce the segments-as-topic (SAT) methodology, a four-stage process that combines automation and human expertise to assess candidate topics for vocabulary inclusion. In the SAT generation stage, a topic is formulated and refined through collaboration with domain experts, and then a sentence-level semantic similarity model retrieves corpus segments semantically aligned with the topic. The SAT expansion stage uses this seed set to find additional semantically similar segments, which are iteratively accepted or rejected to build a final segment set. During the review stage, a panel of scholars evaluates the topic for inclusion. In the integration stage, all segments in the final segment set are automatically tagged with the new topic. We apply this methodology to the Comparative Constitutions Project vocabulary that tracks over 330 topics in national constitutions, and demonstrate the addition of three new topics to the vocabulary. The SAT approach balances computational efficiency with expert judgment, offering a systematic, user-friendly, and replicable framework for social scientists to expand domain-specific vocabularies.