I checked 7 public opinion journals on Tuesday, July 21, 2026 using the Crossref API. For the period July 14 to July 20, I found 13 new paper(s) in 7 journal(s).

International Journal of Public Opinion Research

Democratic regime support in Taiwan and South Korea: a latent class approach
Christian Schafferer
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Why do some states sustain democratic governance while others fail? Research points to citizens’ attitudes, yet measuring support is difficult, especially in Asia, where endorsement of democracy coexists with the acceptance of authoritarian practices. Chu and Huang (2010) propose a framework classifying citizens into four clusters based on democratic legitimacy and liberal values using Asia Barometer Survey data. This study argues that economic change, digital media expansion, and generational shifts have fragmented political attitudes in postmodern societies like Taiwan and South Korea, fostering “modular” identities with cross-cutting views. Using latent class analysis (LCA), it uncovers hidden groupings and shows LCA captures nuances missed by conventional typologies, offering a more flexible tool for assessing democratic support and resilience.

Journal of Elections, Public Opinion and Parties

Party position knowledge, ideological consistency, and the representation gap: evidence from the 2024 European elections
William John Atkinson
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Disrupting democracy: how informational pathways and misinformation shape trust in election results
PatrĂ­cia Rossini, Camila Mont'Alverne, Antonis Kalogeropoulos
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Black and Latino American support for cross-ethnic coalitions: evidence from two conjoint experiments
Kenicia Wright, GĂĽneĹź Murat TezcĂĽr
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Affective polarization and electoral turnout: testing the moderating role of political sophistication on the Italian case
Antonino Castaldo, Danilo Di Mauro, Vincenzo Memoli
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Journal of Official Statistics

Strengthening Digital Survey Implementation for Official Statistics Using Text Mining: Evidence from Feedback Dataset of the Digital Domestic Tourism Survey 2024
Hanun Nabila Azis, Erna Nurmawati, Teguh Sugiyarto, Eko Rahmadian
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The shift toward non-traditional data sources has made Mobile Positioning Data (MPD) a key tool for more flexible and scalable data collection. By utilizing SMS-to-web invitations from network providers, this method improves coverage, timeliness, and cost efficiency, though it still faces hurdles regarding response rates, respondent burden, and respondent trust. This study analyzes respondent feedback from the Digital Domestic Tourist Survey 2024 using sentiment analysis and topic modeling. The most frequently discussed aspects were survey application reliability (64.48%), web questionnaire presentation (63.53%), perceived ease of use (63.09%), and questionnaire content (59.80%). Positive sentiment dominated all aspects, accounting for at least 61% of responses, indicating an overall favorable perception. Topic modeling of survey question variables produced a coherence score of 0.4199. Higher coherence was observed for the positive sentiment dataset (0.5447) compared to the negative sentiment dataset (0.4869), suggesting a well-structured alignment with the study’s framework. Despite the generally positive evaluation, improvements are needed in application reliability, questionnaire design, incentive clarity, data collection methods, and communication strategies to reduce respondent burden, and enhance participation in MPD-based SMS-to-web surveys.

Journal of Survey Statistics and Methodology

Using Opt-In Non-Probability Surveys to Estimate COVID-19 Infection and Vaccination Rates
Alexi Quintana-Mathé, Ata A Uslu, Jason Radford, James N Druckman, Kristin Lunz Trujillo, Alauna Safarpour, Katherine Ognyanova, Matthew A Baum, Jonathan Schulman, Roy H Perlis, Mauricio Santillana, David Lazer
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Public health crises demand timely surveillance of disease and interventions. This was a substantial challenge during COVID-19 in the United States due to the federalized response. States varied widely in infection and vaccination rates. Continuous state-level probability samples for tracking were unavailable and would have been logistically and financially onerous. Administrative data eventually became inaccurate. We thus evaluate the accuracy of large-scale opt-in non-probability state and national samples (from the Covid States Project) that estimated infection and vaccination rates on a near-continuous basis. The estimates aligned closely with a high-quality national probability sample and state-level administrative data (when such data were reliable). We offer evidence that the success of the surveys, compared to other less accurate non-probability surveys, may have stemmed from the successful recruitment of respondents with low trust in health institutions (i.e., individuals who often avoid surveys). We conclude by discussing how the Covid States Project can inform further data collection efforts requiring extensive spatial and temporal coverage.
Probability Snowball Sampling from Graphs, with an Application to Actor-Actor Network
Melike Oguz-Alper, Li-Chun Zhang
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One can study any real graphs based on the subgraphs obtained by probability sampling. This is useful when it is either infeasible or too costly to process the whole graph due to various reasons. We consider probability snowball sampling (SBS) from graphs, where the initial node sample is selected with known probabilities, and each following wave of observation is carried out exactly as specified. The literature on design-based inference for probability SBS from graphs has limited scope, and there does not exist any design-unbiased strategy that generally makes use of units (or networks of units) obtained after the initial sample. In this paper, we adopt a unified framework for T-wave SBS from graphs, where the study units are not limited to the nodes in the graph but may be any finite-order subgraphs, say, triangles, cycles, or stars. We propose two practical design-unbiased strategies for estimating the corresponding graph totals, which considerably extend the previous approaches to probability SBS. The practitioners are thereby provided with richer choices to improve sampling efficiency, which we will demonstrate with an application to the actor-actor network from IMDb.
Causal Discovery with Incomplete Data Integrated from Multiple Sources
Asrat Ayele Woldemariam, Yingchun Zhou, Yuqi Qiu
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Missing data poses fundamental challenges for causal discovery in observational studies, particularly when inferring directed acyclic graphs that represent underlying cause-and-effect relationships. Traditional approaches to handling missing data, such as complete-case analysis, can introduce biases and reduce the reliability of causal inferences. While multiple imputation via chained equations represents the current standard for handling missing data, its effectiveness for causal discovery is limited by both the absence of complete observations and the lack of principled methods for aggregating causal graphs across imputed datasets. This work introduces a novel unified framework that integrates causal structure learning directly into the imputation process. The key innovation lies in using preliminary adjacency information to estimate missingness propensity scores, which are then incorporated into the imputation model together with source indicator variables to generate more accurate estimates of missing values. The conditional independence test is implemented with source indicators included in the conditioning set, and by pooling test statistics rather than graph structures across multiple imputations, the proposed approach addresses the traditional challenge of graph aggregation while maintaining statistical rigor. Simulation studies demonstrate that the proposed framework achieves better performance compared to the traditional approach across all relevant metrics. The proposed framework is adapted to handle complex survey data by incorporating survey weights and survey design in both data imputation and conditional independence tests for causal discovery. When applied to childhood anthropometric survey data from Mali, the proposed approach produces results that better align with expected outcomes. By combining imputation and causal discovery in a unified process while accounting for survey design, it provides survey researchers with a practical tool for integrating and analyzing incomplete, complex survey data from multiple sources or time points.

Politics, Groups, and Identities

Streamlining measurements of gendered personality: integrating the Bem Sex-role inventory into national surveys
Curran Holden, Deborah Schildkraut
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Public Opinion Quarterly

Reevaluating News and Opinion Dynamics in the Iraq War: New Evidence for Social Identification Effects
Zachary Jablow, Scott L Althaus
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What causes changes in popular support for American wars? Competing theories posit that either the tone and content of wartime news or social identity cues conveyed through mass media should play the larger role. We offer new evidence in this debate by analyzing a novel set of rolling cross-sectional opinion data collected continuously over 13 months during an early phase of the Iraq War and pairing it with data on news intensity and the evaluative tone of news coverage during that same period. Contrary to conventional information updating explanations of war support but consistent with a social identification process, we find that greater intensity of wartime news coverage is associated with increases in aggregate support even after controlling for the evaluative tone of that news coverage.
Compromised Labor Rights and Regime Legitimacy Under Authoritarianism
Hsu Yumin Wang
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How do citizens in authoritarian regimes respond when state promises go unfulfilled? In many such regimes, leaders enact prolabor legislation to signal responsiveness, yet enforcement often falls short in practice. This research note provides the first experimental evidence regarding how enforcement gaps in prolabor legislation shape mass political attitudes under authoritarian rule. Drawing on a preregistered survey experiment in China, I show that the absence of enforcement significantly erodes political support: Unmet expectations surrounding labor rights produce a measurable decline in trust in government. Partial enforcement, in contrast, generates markedly less negative public reactions—an effect that seems to be driven by the perceived material benefits associated with this enforcement. A follow-up study incorporating a list experiment to detect preference falsification replicates the core findings. By examining mass reactions to varying levels of labor rights enforcement in authoritarian contexts, this study advances our understanding of authoritarian legitimation strategies and clarifies the conditions under which cooptation can sustain public support.

Social Science Computer Review

Does Negativity Pay Off? The Impact of Political Party Attributes on the Use and Performance of Negative Campaigning in Digital Political Advertising
Hanna Paulke, Simon Kruschinski, Márton Bene, Jörg Haßler, Daniel Jackson, Darren Lilleker, Melanie Magin, Uta Russmann
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While research has shown how different political actors adapt engagement-triggering strategies in organic election campaigns on social media platforms, we know little about how such strategies are used in paid political advertising and how they affect algorithmic ad performance. To address this gap, this study analyzes how party attributes shape spending on, impressions of, and the cost efficiency of negative campaigning in digital political advertising. Manually content-analyzing all Facebook ads from 48 parties across 10 countries during the European Parliament election in 2019, we find that, on average, parties do not allocate more resources to negative ads, which also do not achieve higher impression counts and are even associated with higher costs per 1,000 impressions (cost efficiency). However, party characteristics matter: Whereas opposition parties invest more in negative campaigning and consequently gain more impressions than government parties, negativity only pays off in terms of cost efficiency for extreme parties. Our findings contribute to a better understanding of the interplay between advertiser characteristics and content strategies in shaping ad use and performance within commercial marketing infrastructure, emphasizing the need for greater transparency to ensure fair competition in digital political advertising.