I checked 7 public opinion journals on Thursday, May 14, 2026 using the Crossref API. For the period May 07 to May 13, I found 12 new paper(s) in 4 journal(s).

Journal of Elections, Public Opinion and Parties

The politicization of attitudes towards misinformation: how ideology shapes epistemic beliefs, normative judgments, and policy support
Mathieu Lavigne
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Comparing Trump to Hitler: public opinion in Austria, Denmark, and Spain
Alexi Gugushvili
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First-time voter boost of turnout: new identification strategy
Maiko Shoji, Kentaro Fukumoto
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Compulsory voting and youth representation in parliaments
Daniel Stockemer, Kamila Kolodziejczyk, Avery Chalmers, Sam Maher, Lauren Garcia
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Journal of Official Statistics

Calibrating Nonresponse Bias: A Cautionary Tale
Raimund Wildner, Volker Bosch, Florian Meinfelder
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Low response rates due to unit nonresponse have always been a ubiquitous problem in survey-based empirical research, and calibration is a popular method to adjust for bias caused by unit nonresponse. Typically, some external information on the true population quantities of margins for some calibration variables is available, and sometimes also of higher-order interactions. Weighting algorithms try to adjust the sample to these external benchmarks. It is generally assumed that even if the underlying missingness mechanism of the unit nonresponse is non-ignorable, weighting will at least alleviate the severity of the bias. We discuss data situations where weighting under a missing at random (MAR) assumption adjusts the sample correctly but still increases the bias for the analysis model, and we describe strategies for identifying auxiliary variables that are less susceptible to these unwanted effects.
Hierarchy-Aware Heterogeneous Graph Neural Network for Occupation Title Coding
Yi Xie, Wenbin Zhu
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Occupation coding encodes job titles into standard occupation labels, which is effective for data processing but tedious. Research proves that classic machine learning is effective, but accuracy needs further improvement. We construct a real data set with 881 occupation categories, including 41,297 pairs of job titles and corresponding labels. We design a hierarchy-aware heterogeneous graph neural network, combining prior knowledge from occupation category trees and synonyms. Results show our model outperforms other methods by 7.62% on micro-F1. It also alleviates the dependence on data as it achieves 52.28% on micro-F1 with only 30% of the original training data set.

Politics, Groups, and Identities

Community-based leaders and civically engaged research: lessons from the remaking of the Latinx Organizational Archives Project
Angie Bautista-Chavez, Dulce Juarez Aguilar, Andrea Whiting, Victoria Villalba
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The legacy of American territorial imperialism and its consequences for civil rights
Jordan Carr Peterson
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Public Opinion Quarterly

Computer-Assisted Mobile Phone Interviews in Low- and Middle-Income Countries Through a Total Survey Error Framework
Abigail R Greenleaf, Huguette Diakabana, Charles Lau
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Researchers increasingly use computer-assisted telephone interviewing (CATI) via mobile phones in low- and middle-income countries (LMIC). A nascent methodological literature explores representation and measurement error in these surveys, but knowledge is disparate, siloed across disciplines, countries, and research designs. Using the total survey error framework, this research synthesis summarizes findings from peer-reviewed methodological research on CATI in LMIC. We used a scoping review methodology to identify and review 38 peer-reviewed journal articles to answer two research questions: (1) Which study designs, topic areas, and total survey error components have been examined in CATI mobile phone surveys conducted in LMIC? and (2) What does the research say about representation and measurement errors in CATI mobile phone surveys in LMIC? Based on these findings, this research synthesis highlights when, where, and how CATI surveys can be used across LMIC.
Who Reads Criticism Matters: How Selective Exposure Affects Public Backlash to Foreign Shaming
Jamie J Gruffydd-Jones
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Recent experimental studies have found that foreign shaming can be counterproductive, engendering more positive public opinion toward a target government. This paper shows that whether foreign shaming in fact leads to this kind of backlash is dependent on citizens’ exposure to the shaming. A modified participant preference trial finds that less nationalist Chinese citizens are significantly more likely to choose to read about American criticism of COVID-19 policies in China. While the criticism has no impact on those respondents who choose to read it, it significantly increases support for the Chinese government among those who choose not to. These findings demonstrate the importance of understanding which members of the public are exposed to international actions, and how campaigns to highlight these actions may make a backlash more likely.
Bad Mood Rising? Assessing Scalar Invariance Violations with Comparative Democratic Support Data
Philip Warncke, Ryan E Carlin
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The advent of nearly global estimates of democratic mood has caused genuine optimism for comparative investigations into the linkages between public opinion and democracy. Scholarly enthusiasm in this field has particularly been boosted by recent claims that measuring latent democratic support with hierarchical IRT models overcomes differential item functioning (DIF)—a well-known challenge that typically foils the comparability of latent constructs across time and space. Focusing specifically on DIF-induced violations to scalar measurement invariance, we show mathematically and with statistical simulations that no commonly used latent variable modeling framework, including hierarchical IRT, is immune to bias stemming from systematic DIF. While some models can fully accommodate measurement invariance violations that are completely random between nations and across items, they begin to falter as soon as such violations exhibit a directional bias, that is, if respondents from different countries interpret or appraise survey items systematically differently. Equipped with democratic mood data from Latin America, we present suggestive evidence that systematic, directional bias in DIF is far more prevalent than random measurement noninvariance. We conclude with a number of practical recommendations for public opinion researchers to mitigate measurement invariance violations in their own work.
Do Group-Based Inequalities Feel More Unjust? Experimental Evidence from Economically Disadvantaged Groups in India, South Africa, and the United States
Lasse Egendal Leipziger, Laurits Florang Aarslew, Matias Engdal Christensen
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Macro-level research finds that group-based inequalities are more likely than interpersonal inequalities to spark political conflict and instability. However, the individual-level mechanisms driving this relationship remain empirically underexamined. We test the central hypothesis that group-based disparities evoke stronger feelings and perceptions of injustice than interpersonal inequalities. Drawing on three preregistered priming experiments among disadvantaged groups in India (n = 1,600), South Africa (n = 1,600), and the United States (n = 3,000), we find limited evidence that intergroup inequality is perceived as more unfair and evokes stronger feelings of injustice than interpersonal inequality. Our findings question the view that ethnic inequalities are perceived as particularly unfair at the individual level, suggesting that their link to conflict may instead operate through other micro-level mechanisms.