I checked 7 public opinion journals on Wednesday, June 24, 2026 using the Crossref API. For the period June 17 to June 23, I found 9 new paper(s) in 4 journal(s).

Journal of Official Statistics

Re-Expressing the Proxy Pattern-Mixture Model as a Selection Model to Assist with Sensitivity Analysis
Seth Adarkwah Yiadom, Rebecca Andridge
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Proxy pattern-mixture models (PPMMs) have previously been proposed as a model-based framework for assessing the potential for nonignorable nonresponse in sample surveys and nonignorable selection in nonprobability samples. One defining feature of the PPMM is the single sensitivity parameter, Ď• , that ranges from 0 to 1 and governs the degree of departure from ignorability. While this sensitivity parameter is attractive in its simplicity, it may also be of interest to describe departures from ignorability in terms of how the odds of response (or selection) depend on the outcome being measured. In this paper, we re-express the PPMM as a selection model in order to better understand the underlying assumptions of the PPMM and the implied effect of the outcome on nonresponse (or selection). The selection model that corresponds to the PPMM is a quadratic function of the survey outcome and proxy variable, and the magnitude of the effect depends on the value of the sensitivity parameter, Ď• (missingness/selection mechanism), the differences in the proxy means and standard deviations for the respondent and nonrespondent populations, and the strength of the proxy as measured by the correlation between the outcome and the proxy in the respondent/selected sample. Large values of Ď• (beyond 0 . 5 ) may result in unrealistic selection mechanisms, and the corresponding selection model can be used to establish more realistic bounds on Ď• . We illustrate the results using a home pricing dataset extracted from the China Family Panel Studies.

Journal of Survey Statistics and Methodology

A Pathological Investigation of Questionnaire Translation: Type, Frequency, and Severity of Questionnaire Translation Errors Made by Iranian Researchers
Nasimeh Nouhi Jadesi, Marziyeh Sadeghzadeh
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The present study investigated and assessed translation errors committed by Iranian researchers in the process of translating questionnaires from English into Farsi (Persian) (the official language of Iran) within the context of questionnaire validation studies. Employing a measurement equivalence/invariance analysis, this research applied the evaluation framework proposed by Behr (2023) to compare the original English versions of questionnaires with their translated Farsi counterparts, based on the premise that fewer errors correlate with higher translation quality. The dataset for evaluation was compiled through systematic database searches, focusing on questionnaires on women. A total of 14 questionnaires meeting the inclusion criteria, comprising 292 items, were identified. Two researchers independently compared and evaluated the quality of the source and translated versions of each questionnaire, followed by online sessions in which they engaged in dialogic discussions to share their coding and perspectives. The questionnaires were examined for translation errors, and the frequency of each of the seven error types outlined in the framework was quantified, exemplified, and analyzed. Findings revealed that the majority of errors (88 percent) occurred at the linguistic level, encompassing general translation issues such as semantic accuracy, stylistic appropriateness, and grammatical correctness, whereas fewer errors pertained to questionnaire-specific translation challenges, including cultural adequacy, terminological consistency, and layout/presentation. Additionally, the study discussed the possibility of introducing some refined manifestations of error types mentioned by the framework. Based on these findings, practical recommendations were provided for researchers involved in questionnaire translation and for journal reviewers and editors overseeing such work.

Public Opinion Quarterly

The Microfoundations of Negotiated Peace: Generalized Trust and Support for Peace Processes in Colombia and Guatemala
Ryan E Carlin, Gregory J Love, Jennifer L McCoy, Jelena Subotic
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Why do citizens support negotiated solutions to internal conflicts? We develop a theoretical framework drawing insights from prosocial generalized trust orientations and classic models of public opinion. We expect generalized trust—a baseline belief in the trustworthiness of others as members of a shared moral community—to be positively associated with support for negotiated peace. Original survey and behavioral data collected during the peace talks between the Colombian government and the Fuerzas Armadas Revolucionarias de Colombia (FARC) and survey data following the Guatemalan peace agreement are consistent with this hypothesis. Standard public opinion models suggest that this association should strengthen as negotiated peace grows in salience, a hypothesis compatible with rolling cross-sectional survey analyses and placebo tests surrounding Colombia’s 2016 peace referendum. By linking prosocial generalized trusting dispositions to attitudes toward negotiated peace outcomes, our findings advance our knowledge of the microfoundations of international relations and conflict resolution.
(Mis)information, Polarization, and Trust in Elections: Longitudinal Evidence from Canada
Mathieu Lavigne, Holly Ann Garnett, Aengus Bridgman
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Scholars have suggested that we are experiencing a global crisis in trust in elections, with political polarization and misinformation often cited as reasons for distrust. However, empirical evaluations of perceptions of election integrity over time remain scarce. Using data from the Canadian Election Study from 2008 to 2021 and the Media Ecosystem Observatory from 2021, we show that trust in electoral management bodies, measured in terms of confidence, satisfaction, and fairness, has significantly declined since 2019 among Canadians with a right-wing ideology or who support right-wing parties. We find evidence that rising affective polarization, right-wing and social media consumption, and belief in US-based election fraud narratives are associated with significant trust declines in electoral integrity. The results identify key factors linked to the recent politicization of citizens’ perceptions of election fairness and have important implications for the health of our democracy.
Disentangling the Three Facets of Mass Ideological Polarization: A Network Approach Across 78 Societies
Yufan Guo, Yilang Peng, Tian Yang
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Public opinion research has intensively examined mass ideological polarization, often linking it to outcomes that threaten key democratic ideals. Both theory and empirical evidence suggest that it comprises multiple manifestations, with their relationships, however, remaining largely unclear. Meanwhile, previous studies have seldom conducted cross-contextual comparisons, which are essential for understanding how macrostructural factors shape mass polarization. These limitations partly arise from the methodological challenges that hinder consistent measurement across diverse contexts. To fill these gaps, this study introduces a belief network approach to compare three core manifestations of mass ideological polarization: disagreement, symbolic-operational ideological alignment, and within-operational ideological alignment. Using data from 78 societies in the World Values Survey and the European Values Study (2017–2023), we employ improved network-based measures that capture ideological alignment without relying on context-specific assumptions. Surprisingly, our findings reveal substantial cross-national variation and contrasting correlations among these manifestations, indicating that mass ideological polarization is neither singular nor universal. We further apply the network approach to examine how modernization, a critical macrostructural factor, shapes ideological polarization, showing that it predicts lower disagreement but higher ideological alignment. By offering a scalable and context-adaptable tool for comparative public opinion research, this study proposes an expanded theoretical framework of mass polarization, highlighting the need to distinguish its diverse manifestations and their structural drivers across global contexts.
Enhancing Participation in Web Tracking Studies Through Monetary Incentives: Experimental Evidence from an Academic Data Collection Infrastructure
Judith Gilsbach, Joachim G Piepenburg, Frank Mangold, Sebastian Stier, Bernd WeiĂź
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Web tracking studies provide unique insights into digital behavior but face challenges related to participant recruitment and attrition. This study examines how different monetary incentive structures—unconditional and conditional—and incentive amounts affect key recruitment stages: consent to participate in web tracking, installation of tracking software, and attrition after installation. To assess the effectiveness of monetary incentives across probabilistic and nonprobabilistic recruitment methods, we implement a preregistered experiment with 1,862 participants in the GESIS Panel.dbd—an academic infrastructure for collecting and linking digital behavioral data with survey data in Germany. Results show that unconditional incentives increase consent rates, whereas conditional incentives more effectively foster installation and, in particular, sustain participation after installation. While probabilistic recruitment poses challenges for initial consent, participation levels hereafter remain comparable across recruitment methods. These findings contribute to the development of cost-effective incentive strategies aimed at enhancing participation in web tracking studies.
Simplified or Misunderstood? Rethinking How We Measure Americans’ Abortion Attitudes in the Post- Roe Era
Natalie Hernandez
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Standard measures of abortion attitudes, such as the American National Election Survey’s four-point scale, do not capture public opinion on the dimensions that structure the post-Roe abortion policy landscape. While these traditional measures rely on broad, categorical distinctions, contemporary policy debates increasingly hinge on two specific factors: the reason for seeking an abortion and the gestational age of the fetus. To address this mismatch, I develop an original survey instrument that measures opinion along these dimensions. Using data from a large-scale survey (N = 69,752), I show that continuing to use the ANES scale introduces partisan-driven, nonrandom measurement error. Specifically, Democrats and Republicans who select the same ANES response hold systematically different policy preferences, and respondents with comparable policy preferences select different ANES answer choices depending on their partisanship—with Democrats anchoring their answers to a more permissive baseline. These findings underscore the risk of relying on broad-based attitudinal scales that do not evolve alongside the policy debates they are meant to capture.
The Relevance of Culture: Collectivism Reduces Negativity Biases in Political Trust
Baowen Liang
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In political science, extensive research has explored negativity biases in citizens’ attitudes and behaviors. In particular, we know that citizens’ political evaluations tend to be more strongly influenced by negative than positive perceptions of traits, events, and policy outcomes. In this paper, I argue that culture is a significant yet understudied correlate of negativity biases. A multilevel analysis using the World Values Survey (WVS) demonstrates that the negativity bias in political trust weakens as a society’s level of collectivism increases. Next, I explore the effect of cultural values at the individual level with data from the Asian Barometer Survey (ABS). In line with the results from the WVS, I find that collectivism reduces the negative-positive asymmetry in citizens’ political trust based on perceived institutional performance. These results suggest that negativity biases should not be universally assumed as a defining feature of citizens’ attitudes toward government across different cultural contexts.

Social Science Computer Review

Artificial Intelligence in Government Weakens Citizens’ Affective Ties With Public Employees: Results From a Vignette Experiment
Pascal D. Koenig, Sveinung Arnesen
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This article examines how citizens’ perceptions of public employees in the education system change as these employees adopt artificial intelligence (AI) in their work. It argues that the use of AI matters for citizens’ perceptions of decision-making not only due to AI system characteristics, but also because AI adoption alters the perceived relationship between citizens and public organizations. Rooted in assumptions of social cognition theory, the analysis tests how information about AI use by public employees alters the perceived warmth of these employees and thereby affects the acceptability of decision-making. The analysis is based on a pre-registered vignette experiment and a sample of 4,569 participants from Norway. It finds that AI use decreases both the perceived warmth and competence of public employees, that these evaluations negatively bear on the overall acceptability of decision-making, and that the effect of AI use is stronger for public employees more directly interacting with others.