I checked 7 public opinion journals on Tuesday, March 17, 2026 using the Crossref API. For the period March 10 to March 16, I found 8 new paper(s) in 3 journal(s).

Journal of Elections, Public Opinion and Parties

Participating in a presidential nomination process during a pandemic
Caitlin E. Jewitt, Gregory Shufeldt
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Journal of Survey Statistics and Methodology

Direct-assisted Bayesian unit-level modeling for small area estimation of rare event prevalence
Alana McGovern, Katherine Wilson, Jon Wakefield
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Small-area estimation using survey data can be achieved by using either a design-based or a model-based inferential approach. Design-based direct estimators are generally preferable because of their consistency, asymptotic normality, and reliance on fewer assumptions. However, when data are sparse at the desired area level, as is often the case when measuring rare events, these direct estimators can have extremely large uncertainty, making a model-based approach preferable. A model-based approach with a random spatial effect borrows information from surrounding areas at the cost of inducing shrinkage. As a result, estimates may be over-smoothed and inconsistent with design-based estimates at higher area levels when aggregated. We propose two unit-level Bayesian models for small area estimation of rare event prevalence, which use design-based direct estimates at a higher area level to increase consistency in aggregation. This model framework is designed to accommodate sparse data obtained from two-stage stratified cluster sampling, which is particularly relevant to applications in low- and middle-income countries. After introducing the model framework and its implementation, we conduct a simulation study to evaluate its properties and apply it to the estimation of the neonatal mortality rate in Zambia, using 2014 Demographic Health Surveys data.

Public Opinion Quarterly

Mia Costa. How Politicians Polarize: Political Representation in an Age of Negative Partisanship
Andrew M O Ballard
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Designing Passwords for Web Survey Access: The Effects of Password Length and Complexity on Survey and Panel Recruitment
Georg-Christoph Haas, Marieke Volkert, Stefan Zins
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Online probability panels that recruit participants via postal invitation letters use passwords to manage access to the survey. While previous research has examined primarily whether providing a password affects response rates, less attention has been given to the impact of password strength, defined by length and complexity, on response propensities. Password length refers to the number of characters in a password, while complexity refers to the set of characters (e.g., lowercase letters, digits). This study evaluates the influence of password length and complexity on various participation levels (i.e., survey access, response rates, and panel registration) as well as the propensity to consent to data linkage and item response rates for income questions. We conducted an experiment in the first wave of a German online probability survey and manipulated password length and complexity. Additionally, we included a group using the default length and complexity settings (eight uppercase letters) of the survey hosting service. The participants were randomly assigned to one of these groups. The findings indicate that longer and more complex passwords increase both participation rates and the propensity to consent to data linkage between survey and administrative data.
Beyond Surveys: Leveraging Real-World Events to Validate Behavioral Measures of News Exposure
Ana S Cardenal, Ludovic Terren, David N Hopmann, Silvia Majó-Vázquez, Peter Van Aelst, Alon Zoizner
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Do people learn about the political world through online media? We address this question by exploiting an unexpected global news event—the Russian invasion of Ukraine—and by developing exogenous measures of media exposure based on three months of web-tracking data from five advanced democracies. Our analysis differentiates between visits to general news domains and visits to politically relevant and Ukraine specific articles, with the latter identified using machine-learning techniques. We validate these exposure measures through multiple approaches, including their capacity to predict knowledge about the Russian invasion. Our findings underscore the importance of granularity. Visits to—and time spent on—Ukraine-related articles emerge as the only significant predictor of surveillance knowledge, whereas broader indicators, such as domain-level visits, show no significant effects once self-reported exposure and other key covariates are taken into account. We conclude by discussing the substantive and methodological implications of these findings.
How Empathy and Partisanship Affected Attitude Changes Following the Assassination of Shinzo Abe: Evidence from Panel Surveys
Zeyu Lyu, Susumu Cato
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Shinzo Abe, the longest-serving prime minister in Japan’s postwar history, was assassinated on July 8, 2022, triggering widespread social reactions and shifts in public opinion. This study investigates the effects of the assassination on attitudes toward Abe using panel data from before and after the assassination. Our results suggest that, overall, attitudes toward Abe significantly improved after the assassination, and that the extent of attitude change varied between groups. Specifically, we find that individuals with strong empathy were more likely to improve their attitude toward Abe, indicating a pattern of emotion-driven attitude change. Our analysis also suggests that partisanship may have shaped individuals’ attitude changes. Specifically, individuals with opposing party preferences were more resistant to attitude change, whereas partisan proximity facilitated positive reassessments. Moreover, the influence of partisanship on attitude change depended on its intensity. Individuals without sustained and strong party preferences were more susceptible to attitude change following the assassination. Overall, this study provides empirical evidence for attitude changes due to political violence and has implications for our understanding of the mechanisms of such attitude changes.
A Demonstration of Propensity-Score Weighting to Adjust a Social Media Nonprobability Sample Survey of Political Attitudes
Michael S Pollard, Michael W Robbins, Max Griswold
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Interest in using nonprobability online samples continues to grow despite concerns about selection bias. Many methods exist for adjusting nonprobability data so it may yield generalizable inferences. Here we investigate whether a propensity score weighting method can balance differences between a probability sample and a nonprobability sample of Twitter (now X) users to evaluate the feasibility of using social media data for producing generalizable inferences on public opinion. We fielded identical surveys to 2,001 probability-sampled respondents (June 30–July 22, 2022) and 949 Twitter users (March 1–July 13, 2022); final analytic sample sizes were 1,972 and 822, respectively. The nonprobability sample differed significantly in demographic characteristics (younger, lower income, higher educational attainment), and broadly endorsed significantly more liberal attitudes toward a range of political and policy issues than the probability sample. We show that the propensity score weighting procedure, using demographics, techno/psychographics, and political ideology, reconciles differences between the samples for 25 of the 27 attitudes assessed. The results demonstrate the feasibility and utility of the propensity score weighting procedure to replicate a probability sample with nonprobability social media data and add to the literature on the use of nonprobability samples to draw population-level inferences.
Levels of Office and Voter Accountability for Democratic Norm Violations
Tadeas Cely, Marc S Jacob, Sean J Westwood
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Political candidates in the United States increasingly endorse antidemocratic actions across all levels of office, yet most research focuses on federal races. A key concern is that norm-violating candidates at lower levels may ascend to higher office, posing broader risks to democratic stability. This study examines whether voters are more or less likely to defect from copartisan norm violators at lower levels of office. Across two preregistered survey experiments with 5,000 US respondents, we find that defection rates remain consistent across local, state, and federal races, regardless of the number or type of violations. These findings suggest two interpretations: optimistically, voters are not more forgiving of norm violations in lower-level elections; pessimistically, even where the stakes appear lower, voters are also no more likely to hold norm violators accountable. Our results are consistent with the nationalization of voting behavior and the risk of a candidate pipeline that enables norm violators to rise unchecked.