I checked 7 public opinion journals on Thursday, June 11, 2026 using the Crossref API. For the period June 04 to June 10, I found 7 new paper(s) in 4 journal(s).

Journal of Official Statistics

A Note on the Additive Decomposition of GEKS Indexes
Steve Martin
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It is often useful to decompose an index number into the contribution of each product toward the total index, and consequently there are several well-known decompositions for bilateral indexes. In this note, I extend these decompositions to cases where bilateral indexes are made into multilateral GEKS indexes. Although the result is primarily of theoretical interest, it shows how decompositions based on a bilateral index can be extended to a multilateral index, and highlights the challenge of decomposing GEKS indexes.
Beyond Survey Length: Understanding Respondent Perceptions of Burden
Erica C. Yu, Brandon Kopp, Victoria R. Narine
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Respondent burden is complex and represents more than just the time spent completing the survey. With this research, we highlight the importance of looking beyond objective measures to understand the respondent’s perception of the survey-taking experience. First, we asked participants to tell us in their own words about the term “burden” and how surveys and survey questions can be burdensome. We identified themes in how respondents think about surveys that can inform targeted approaches to reducing respondent burden. We then tested the idea that perceptions of burden may not adhere to any objective measure of what it means for a survey to be burdensome. Through survey instructions, we presented different frames of reference to dissociate perceptions of survey length from actual survey length and analyzed the effect on ratings of burden. Our research suggests that factors like repetition, disorganization, and perceptions of pointlessness are key to respondents’ understanding of burden. Different frames of reference translated to significant differences in both perceptions of survey length and perceptions of burden, regardless of actual survey length. To improve respondents’ survey-taking experience, survey designers must go beyond survey length to consider perceived burden.
Book Review: Robust Small Area Estimation: Methods, Theory, Applications, and Open Problems , by Jiming Jiang and J. Sunil Rao JiangJimingRaoJ. Sunil. Robust Small Area Estimation: Methods, Theory, Applications, and Open Problems. 2025Boca Raton, FL: Chapman & Hall/CRC. ISBN 9781032488851, 275 pages.
Andreea L. Erciulescu
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Journal of Survey Statistics and Methodology

EXPLORING THE POTENTIAL OF NOVEL PARADATA IN RESPONDENT-DRIVEN SAMPLING
Sunghee Lee, Leng Seong Che
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This study extends the scope of the paradata discussion to respondent-driven sampling (RDS). Unlike traditional sampling, RDS relies on existing social networks within a target population. This unique process provides opportunities to produce novel paradata. Specifically, this study examined two types of paradata in RDS: one based on interviewer observations and the other based on recruitment behaviors ascertained from tracking recruitment coupons. We implemented these paradata features in two independent RDS surveys. In an in-person RDS survey of persons who inject drugs in Southeast Michigan, we implemented an interviewer observation questionnaire. This included questions about interviewers’ assessments of respondents’ understanding of coupon distribution instructions, as well as their expectations regarding respondents’ chances to recruit others and to return for a follow-up interview. These observations predicted recruitment success. In a Web-RDS study of Korean Americans, physical distance between linked respondents (such as a respondent and their recruiter) was determined by tracking recruitment coupons and geocoding respondent addresses. Greater geographic distance was associated with a higher likelihood of serious psychological distress. The results demonstrate that the unique features of RDS offer new avenues for utilizing paradata in both methodological and substantive research. These findings warrant further exploration and development of paradata specific to RDS.

Public Opinion Quarterly

Does Compulsory Voting Improve Democratic Attitudes and Engagement? Quasi-Experimental Evidence from Belgium
Dieter Stiers, Shane P Singh
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For over a century, scholars, policymakers, and advocates have argued that compulsory voting not only boosts turnout but also promotes prodemocratic attitudes and political engagement. However, empirical tests of this claim have been limited by trade-offs between internal and external validity. To address this challenge, we investigate the consequences of compulsory voting for democratic attitudes and engagement using a design that yields relatively credible and generalizable estimates. We exploit a unique quasi-experiment in Belgium, where compulsory voting was recently abolished for local elections in one region but retained elsewhere. To estimate the effects of this policy change, we employ difference-in-differences models using a new panel dataset covering four elections held in 2024. We find that, although the abrogation of compulsory voting caused a sharp drop in turnout, it did not alter democratic attitudes and engagement.
Polling Across Borders: The Promise and Pitfalls of Convenience Samples in a Cross-National Context
Dino P Christenson, Gustavo A Flores-Macias, Sarah E Kreps, Douglas L Kriner
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This study evaluates the performance of two widely used survey platforms, Lucid and Morning Consult, across six diverse national contexts: Brazil, India, Japan, Nigeria, the Philippines, and the United States. We assess the impact of platform choice on sample composition, response quality, political judgments, and treatment effect estimation, focusing on a randomized corruption treatment embedded within the survey. Attention filter passage rates were similar and generally high across countries and platforms, while the percentage of high-frequency survey-takers varied greatly across countries. Our findings reveal significant demographic skews, with both platforms consistently overrepresenting college-educated respondents. Despite these differences, political assessments and estimated average and heterogeneous treatment effects remain broadly consistent across platforms, and in Brazil our estimates largely tracked those from past research with a probability sample. We find some evidence of cross-national variation in the magnitude of treatment effects, but these differences were often platform-specific. These results suggest that convenience samples can provide reliable estimates of causal effects even in diverse contexts. Taken together, our research highlights the trade-offs between cost, speed, and representativeness in global public opinion research, offering insights into the challenges and opportunities of online survey platforms.

Social Science Computer Review

Computational Public Opinion Measurement: A Systematic Review of Methods and Methodological Limitations
Eun Gyo Joung
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Computational Public Opinion Measurement (CPOM) uses natural language processing and machine learning to infer public attitudes from social media. However, the methodological foundations and validity constraints of CPOM remain inadequately documented. This systematic literature review examines the computational methods, sentiment representations, and measurement strategies employed in CPOM research, and identifies the structural and methodological limitations that constrain CPOM as an approach to measurement. This systematic review (PRISMA, 2020) searched seven databases, identifying 56 studies from 5,108 records (2008–2025). Methods shifted substantially across eras: lexicon-based approaches declined from 77.8% (2011–2015) to 28.6% (2021–2025) while deep learning grew from 0% to 40.0%. Despite this technical evolution, empirical validation remains rare. Only 11 of 56 studies (19.6%) computed a quantitative statistic against an external benchmark such as a survey or poll. In other words, methodological sophistication and validation rigour moved in opposite directions: none of the 14 deep learning studies computed a quantitative validation statistic, while all 11 that did used lexicon-based or classical machine learning. The broader field shows an even larger gap: 49.4% of all full-text-assessed papers were excluded for providing no external validation or representativeness analysis at all. The recognition-action gap widened over time: TSE awareness grew from 44.4% to 80.0% across eras while quantitative validation rates fell from 33% to 11%, showing that growing awareness has not translated into action. CPOM must move beyond technical sophistication toward systematic criterion validation, demographic adjustment, and transparent reporting.