I checked 7 public opinion journals on Friday, July 17, 2026 using the Crossref API. For the period July 10 to July 16, I found 12 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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Journal of Official Statistics

Ensuring Data Integrity in Official Financial Statistics: A Review of Hybrid AI and XAI Methods in the Context of Market Efficiency and Value Investing
Krzysztof PodgĂłrski
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The integrity and accuracy of financial data are prerequisites for market efficiency; however, data anomalies and quality issues severely compromise their “fitness for use” in sophisticated decision-making processes, such as value investing strategies. This article reviews the application of advanced artificial intelligence (AI) methods to enhance quality assurance, anomaly detection, and imputation within high-dimensional financial data streams. The paper critically evaluates both statistical-machine learning hybrids (e.g., ARIMA-LSTM) and deep learning combinations (e.g., autoencoder-based GANs), alongside Explainable Artificial Intelligence (XAI) techniques, assessing their utility against the strict auditability requirements of public trust institutions. The synthesized literature suggests that hybrid frameworks can potentially outperform monolithic approaches in detecting nonlinear manipulations and creating “high-fidelity” datasets. Furthermore, the study addresses the “black box” opacity challenge—a major barrier for regulatory and statistical agencies—discussing how methods like SHAP and LIME support, rather than independently ensure, the necessary interpretability of algorithmic decisions. Conclusions indicate that the synergy between the predictive power of advanced AI models and the transparency supported by XAI is a highly valuable component for modern market supervision, enabling effective data validation while supporting institutional accountability.
Beyond Algorithmic Accuracy: Understanding Data Quality in Interactive Occupational Coding with the COARSE Framework
Olga Kononykhina, Malte Schierholz, Frauke Kreuter
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Survey research increasingly relies on automatic tools to classify open-ended occupational data. With the rise of machine learning (ML) and large language models (LLMs), this task is shifting from post-survey coding to live classification during interviews. Respondents actively evaluate algorithmic suggestions, creating a joint human–machine decision process where data quality cannot be assessed by accuracy metrics alone. Drawing on human–computer interaction and survey methodology, we propose the COARSE (Classification Outcomes and Respondent Engagement) evaluation framework, which distinguishes five outcomes: accurate classification, misclassification, omission error, commission error, and appropriate rejection. Evidence from a representative German survey deploying an interactive ML-based instrument (OccuCoDe) shows that respondents cannot reliably safeguard data quality when algorithms fail. Instead of rejecting poor suggestions, they often settle for “good enough” answers. When correct options are present, errors reflect misjudgments of subtle distinctions. When respondents reject machine suggestions, they report higher task difficulty afterward, especially when valid options were overlooked. These results show that data errors extend to human cognition and survey interaction, going beyond established machine learning metrics for algorithmic decision-making. As humans and machines collaborate in the survey-answering process, the COARSE framework offers a new lens to evaluate data quality and improve automated coding systems.
Estimating the Multidimensional Poverty Index in Bogotá (Colombia) Using Satellite Imagery
Juan Sebastián Oviedo, Mario E. Arrieta-Prieto
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This paper proposes an alternative approach to estimating poverty levels using satellite imagery from Bogotá, Colombia, and surrounding municipalities. Two modeling strategies are compared: Convolutional Neural Networks (CNNs) and a newly proposed extension of the Generalized Tensor Regression model based on spatially-weighted aggregations, denoted SWGTR. The models are evaluated using multiple performance metrics, including Average Accuracy (AA), Overall Accuracy (OA), Area Between Curves (ABC), Kappa Index (KI), Mean Absolute Error (MAE), and Cumulative Sums (CS), to determine their effectiveness in capturing the spatial distribution of the Multidimensional Poverty Index (MPI) at the pixel level. The application leverages PlanetScope (PS) satellite images alongside block-level data from the 2018 National Population and Housing Census (CNPV2018). The SWGTR approach demonstrates competitive performance across several metrics, particularly those accounting for the ordinal structure of poverty levels. In addition, we illustrate how the probabilistic outputs of SWGTR can be used to construct pixel-level uncertainty measures, which may inform data collection strategies by identifying regions where predictions are less certain. Overall, the findings suggest that combining spatial modeling with remote sensing data provides a promising avenue to complement traditional survey-based methods, while highlighting important limitations and directions for future research.

Journal of Survey Statistics and Methodology

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

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.
Are the Major “Western” Findings on Attitudes to Immigration Supported by Global Evidence? Mostly Yes, Actually
James Dennison
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The causes of variation in attitudes to immigration have been the subject of extensive scholarship in recent decades, with some factors being repeatedly evidenced. However, these findings are overwhelmingly based on studies from a small selection of “Western” countries. This article makes use of recent World Values Survey data across 66 countries globally to test how generalizable such findings on the determinants of attitudes to immigration are globally. It finds that the most typically identified sociodemographic, economic, and contextual determinants of immigration policy preferences, prejudices, and perceived effects are largely generalizable globally, namely: age, low income, job worries, rurality, not having an immigrant background, social distrust, national migration rate, feeling unsafe in one’s neighborhood, and national media bias. The effects of gender, education, and left-right self-placement are weaker and less generalizable. These findings are robust across world regions. Although the predictive power of all factors tend to be smaller outside of Western Europe, these findings shed light on the more profound, universal, and genuine nature of attitudes to immigration, their likely internal determinants—rather than purely external and contextual ones—and the global reliability of measuring their predictors.

Social Science Computer Review

Profiling Digital Hate: A Multidimensional Measurement Approach Based on the Perspective API
Thomas Kirchmair, Kevin Koban, Jörg Matthes
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The automatic classification of digital hate is a pressing challenge, yet many existing computational models remain opaque and insufficiently evaluated as measurement tools in social science contexts. This study examines the utility of Google’s Perspective API as a measurement instrument by modeling higher-order constructs of harmful discourse (i.e., incivility and intolerance) as outcomes of multiple lower-level behavioral indicators captured by distinct API scores rather than using a single aggregate score as a proxy for complex social behaviors while enabling the evaluation of state-of-the-art black-box classifiers beyond classification metrics. Drawing on 4,000 manually annotated English-language YouTube comments in the context of the Israel-Hamas war, we test whether multiple API scores predict incivility and intolerance using generalized linear and additive models, assess classification performance across non-hateful, uncivil, and intolerant content, and benchmark a recent deep learning model. Results show that Identity Attack is a strong predictor of intolerance, whereas Insult and Profanity are indicative of incivility. While classification performance is somewhat below state-of-the-art deep learning models, our approach offers important advantages: transparency, interpretability, accessibility for non-technical researchers, and potential cross-linguistic applicability. We argue that typology-driven, multi-indicator-based classification provides a practical and theoretically grounded complement to more aggregated black-box models, particularly in human-in-the-loop workflows that can help reduce annotator exposure through pre-filtering of content.