I checked 7 public opinion journals on Saturday, August 22, 2026 using the Crossref API. For the period August 15 to August 21, I found 8 new paper(s) in 5 journal(s).

Journal of Elections, Public Opinion and Parties

ā€˜The people = majority?ā€™ Examining the relationship between populist attitudes and majoritarian decision-making preferences among adolescents
Jaap Van Slageren, Matthijs Rooduijn
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Journal of Official Statistics

Experiments of Quasi-Randomization Estimation for Official Statistics Based on Aggregated Mobile Network Operator Data
Li-Chun Zhang, Marcello Dā€™Orazio, Jens Kristoffer Haug, Tiziana Tuoto, Johan Fosen
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Despite the potentials of data from mobile network operators (MNOs), there exist no official statistics today that are produced from aggregated and anonymized mobile phone signaling data, which in principle are accessible in every country. In the lack of MNO data, we develop and conduct proof-of-concept experiments in Norway and Italy using statistical registers and sample survey data. Viable methods and potential complications are explored and analyzed, such that we are now prepared to negotiate access to MNO data for building the production solutions. Both the estimation methodology and our experimental approach to non-survey big data can be useful to others with similar statistical interests, whether the data arise from mobile phones, transactions, or other sources.
How Does the General Population Think About Smart Surveys?
Barry Schouten, Ilaria Lunardelli, Monica Perez, Barbara Dā€™Amen, Janelle van den Heuvel, Alessandra Nuccitelli, Barbara LorĆØ, Bella Struminskaya, Mateja Zgonec
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The New Ways of Measuring (NWM) or ā€˜smart perceptionsā€™ survey has been conducted in three countries (Italy, the Netherlands, and Slovenia) between September 2023 and February 2024. The aims of the survey were to find clues for improving nudge-to-smart fieldwork strategies, to inform legal officers, and to understand country differences in doing so. The NWM consisted of a two-step approach with a questionnaire on digital skills, hypothetical willingness and corresponding perceptions, and a ā€˜smartā€™ survey including four smart features. We find differences both between countries and between types of features, but also commonalities. We discuss the survey results and provide recommendations for future smart surveys.

Journal of Survey Statistics and Methodology

Using Web Survey Paradata to Analyze Response Quality, Respondent Characteristics, and Survey Estimates: a State-of-the-Art Review and Typology
Vasja Vehovar, Gregor Čehovin
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In the context of web surveys, the term paradata primarily refers to digital traces of the response process. With the increasing prevalence of web surveys, paradata are often perceived as vital for analyzing the methodological and substantive aspects of web surveys. We evaluated these claims through a state-of-the-art literature review. An extensive search process yielded 3,436 screened abstracts and 173 empirical publications that addressed web survey paradata. The results showed that empirical research on such paradata remains relatively limited, characterized by stable publication levels and quite narrow scopes. Specifically, paradata were primarily employed to examine response quality (92 percent of publications), with respondent characteristics (25 percent) and substantive issues (6 percent) being addressed less frequently. In terms of indicators, response times dominated (83 percent), followed by device type (38 percent), while navigation (9 percent), answer changes (8 percent), multitasking (8 percent), and other advanced indicators were used less frequently. In this article, we discuss these results and provide an operational framework that addresses the complexities of integrating paradata into survey design, thereby facilitating the formulation of realistic expectations in survey research practice.
Assessing the Reliability of Household Sampling Using Rooftop Data in India
Douglas Johnson, Anjani Balu, Poornima Ramesh, Linh Vo, Rediet Mulu, Amir Emami
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In settings where comprehensive household lists are unavailable, surveyors typically sample households by first selecting areas as primary sampling units, then conducting a complete listing of all households in each selected area, and finally, sampling households from these household lists. Due to the need to perform a household listing, this approach is costly and sometimes infeasible. Researchers have recently explored using ā€œpin dropā€ techniques to directly sample households within primary sampling units without first conducting a listing. While existing pin-drop methods are far cheaper than the household listing method, it is unclear if they generate unbiased results. We introduce ā€œrooftop samplingā€ (RS), a novel method based on the pin-drop approach that samples households by first sampling rooftops using an open-source dataset of rooftops created by Google and Microsoft. We develop a framework to assess potential biases associated with alternative second-stage sampling strategies like RS. Applying this framework using Demographic and Health Survey data, a ground truth dataset from India, and various field tests, we find that RS introduces minimal biasā€”on the order 1 percentage point for a binary variable. We show that RS is often optimal for small to medium-sized surveys (fewer than 1,000 respondents) or when the household listing method is not feasible.
Going Online With a Telephone Employee Survey: Effects On Coverage, Nonresponse, and Total Selection Bias
Jan Mackeben, Joseph W Sakshaug
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Telephone surveys have historically been a popular form of data collection in labor market research and continue to be used to this day. Yet, telephone surveys are confronted with many challenges, including imperfect coverage of the target population, low response rates, risk of nonresponse bias, and rising data collection costs. To address these challenges, many telephone surveys have shifted to online and mixed-mode data collection to reduce costs and minimize the risk of coverage and nonresponse biases. However, empirical evaluations of the intended effects of introducing online and mixed-mode data collection in ongoing telephone surveys are lacking. We address this research gap by analyzing a telephone employee survey in Germany, the Linked Personnel Panel (LPP), which experimentally introduced a sequential web-to-telephone mixed-mode design in the refreshment samples of the fourth and fifth waves of the panel. By utilizing administrative data available for the sampled individuals with and without known telephone numbers, we estimate the before-and-after effects of introducing the web mode on coverage and nonresponse rates and biases. We show that the LPP was affected by known telephone number coverage bias for various employee subgroups prior to introducing the web mode, though many of these biases were partially offset by nonresponse bias. Introducing the web-to-telephone design improved the response rate but increased total selection bias, on average, compared to the standard telephone single-mode design. This result was driven by larger nonresponse bias in the web-to-telephone design and partial offsetting of coverage and nonresponse biases in the telephone single-mode design. Significant cost savings (up to 50 percent per respondent) were evident in the web-to-telephone design.

Public Opinion Quarterly

An Experimental Comparison of AI-Enabled Semi-Structured Interviews and Fixed Surveys: Respondent Satisfaction, Response Patterns, Quality, and Representation
Amanda Austin, Edward Hohe, Ryan Kennedy, Leib Litman, William Minozzi, Laura Moses
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Artificial intelligence (AI) interviewing offers the potential to enhance the depth and quality of survey research, yet it may also introduce respondent fatigue and unintended consequences for the measurement of political attitudes. From the standpoint of respondents, more extensive interviews impose greater cognitive and temporal burdens, which may reduce engagement or increase satisficing. From the perspective of political behavior researchers, deeper probing may shift the subset of beliefs or arguments that respondents access when forming opinions, thereby altering the distribution and apparent stability of measured attitudes. To investigate these dynamics, we conduct a large-scale, fully randomized experiment. We find that AI-mediated interviewing significantly increases both the volume and specificity of respondentsā€™ articulated rationales, but any decline in participant satisfaction is minuscule and we detect no increase in participant rolloff. However, AI interviews appear to shape subsequent responses: After offering justifications through AI interaction, respondents exhibit greater polarization in follow-up items. This result suggests that AI interviewing may alter the cognitive architecture of opinion formation. These findings both highlight the potential for AI interviewing and underscore the need for studies of how AI interviewing may become a source of attitudinal change.

Social Science Computer Review

Managing Conti: Temporal Dynamics of Managerial Communication in a Ransomware Group
Giovanni Radhitio Putra Sadewo, David Bright, Chad Whelan, JĆ¼rgen Lerner
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Ransomware groups are frequently described as structured enterprises with defined managerial hierarchies, yet most empirical research adopts cross-sectional designs that treat leadership positions as static. This study asks: How do upper- and middle-management actors in a ransomware organisation adjust their communication patterns over time, particularly in balancing coordination and concealment? Focusing on the Conti ransomware group, we examine whether managerial centrality and rank-based communication preferences remain stable or shift across different operational phases. We analyse leaked Jabber chat logs comprising 95,134 cleaned message events exchanged among 287 unique user IDs between June 2020 and March 2022. Actors were classified into upper management ( n = 9), middle management ( n = 9), staff ( n = 81), and affiliates/unassigned ( n = 188). The data were divided into four temporal periods. Using relational event models (REMs) estimated via Cox proportional hazard models with sampled non-events, we model rank-based sending, receiving, and homophily/heterophily effects, interacting these mechanisms with time to capture period-specific dynamics. The findings indicate that upper management was highly active in sending and receiving messages during the early stage of operations, but subsequently withdrew from routine communication. Despite reduced visibility, upper managers consistently exhibited cross-rank (heterophilous) communication rather than insulating themselves through same-rank ties. Middle management, by contrast, remained consistently active across most periods and alternated between cross-rank and same-rank communication patterns, reflecting a flexible intermediary role. Overall, managerial centrality did not persist uniformly over time. This study contributes to the literature by advancing a dynamic, longitudinal perspective on ransomware governance. It demonstrates that managerial embeddedness is temporally contingent and tier-specific, underscoring the importance of event-level and rank-based network analysis. The findings also inform disruption strategies by identifying when and which managerial tiers are most structurally central, highlighting stage-specific vulnerabilities in ransomware organisations.