I checked 7 public opinion journals on Wednesday, November 04, 2026 using the Crossref API. For the period October 28 to November 03, I found 9 new paper(s) in 5 journal(s).

International Journal of Public Opinion Research

Changing abortion attitudes around the world: a within-between analysis of country-level factors
Jeffrey Mitchell, Mikael Hjerm
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There has been a renewed research interest in abortion attitudes in recent years, with a focus on country-level influences in a cross-country perspective. This research typically points to four factors at the country level that should, for different theoretical reasons, influence abortion attitudes: religiosity, gender (in)equality, political freedom, and economic prosperity. We build on this cross-sectional research by analyzing these factors longitudinally using random effects within-between models with the Integrated Values Survey in 88 countries across 32 years between 1990 and 2022. Our results show that political freedom is related to abortion attitudes, but this association exists only cross-sectionally, not longitudinally. Only changes in religiosity and GDP over time were associated with abortion attitudes, and that change in religiosity is the largest macro-level predictor included in our analysis. These results nuance previous literature on abortion attitudes and call into question whether trends in political freedom and gender equality will have the power to influence attitudes on this issue.

Journal of Survey Statistics and Methodology

Does Respondent Gender Matter to the Collection of Household Consumption and Expenditure Data in India?
Sharan Sharma, Pallavi Choudhuri, Sonalde Desai
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Consumption and expense (CE) data from surveys in low-and-middle-income countries (LMICs) are critical to understand social inequalities, formulate policies, and implement targeted benefits. However, little research has examined measurement error in these data. This paper investigates one potential source of measurement error: the respondent’s gender. This is important given the sharp differences in gender roles, attitudes, experiences, and identities in LMICs that may result in different responses from male and female respondents. We analyze CE values from 34 items that were collected from a recent large-scale survey of more than 46,000 households in India; females were respondents in 37 percent of these households. We use multigroup confirmatory factor analysis to examine if data from male and female respondents have measurement equivalence. We then use zero-inflated gamma models to investigate if respondent gender is associated with the prevalence of zero values and the magnitude of non-zero values, using propensity weights to adjust for possible selection biases. Results show that while most CE categories exhibit gender effects, effect sizes are generally modest. The effect magnitudes vary by CE category, and in general, female respondents are associated with more zeros and lower non-zero response values compared to male respondents. However, categories say annual education expenses or 30-day medical expenses see a larger average value for females compared with males. We find that the role of the respondent in shopping for the household and managing its expenses partially explains gender effects. The respondent’s age and education, and help received in answering the CE questions, moderate gender effects on non-zero CE values. We suggest avenues for future research and implications for survey practice.

Politics, Groups, and Identities

Glottophobia in the great white north: investigating linguistic prejudice between French and English speakers in Canada
Evelyne Brie, Adrien Cloutier, FĂ©lix Mathieu, Yannick Dufresne
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Public Opinion Quarterly

Investigating the Utility of LLM-Based “Silicon Samples” in Political Science Conjoint Experiments
Alice Malmberg
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This research note is the first study to explore the viability of using LLM-generated respondents in forced-choice conjoint experiments. I generate naive “silicon samples” using three models, GPT 4o-Mini, Llama 3.2, and Magistral-small-2506, benchmarked to a large sample of live conjoint respondents, allowing for direct comparisons between human and silicon choices. The resulting analyses suggest that naively generated silicon samples are insufficient to simulate human decision-making in conjoint tasks; these results also suggest that there are systemic differences in the way demographic covariates shape LLM and human choices in experimental contexts. In total, this analysis makes two important contributions: it establishes a descriptive baseline understanding of LLM-generated respondent performance in conjoint experiments and suggests that naive silicon samples cannot accurately infer human choices using demographic traits in experimental decision making contexts. Finally, the implications of these results imply that LLMs consider covariates differently than live respondents do in micro-level decision making contexts.
Do Gender-of-Interviewer Effects Differ Across Immigrant Generations? Survey Evidence from Italy
Christina Sarah Hauser
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Interviewer gender has been shown to affect responses to survey questions on gender attitudes and norms, but does its influence vary across groups with different migration backgrounds? I study this question using the Between Origin and Destination (BOnD) survey, conducted in Lombardy, Italy, between 2024 and 2025. Consistent with gender-of-interviewer effects, a type of social desirability bias, female interviewers elicit more progressive responses on a summary index of gender norms and on an index of the acceptability of homosexuality, abortion, divorce, casual sex, and premarital sex. The effect is concentrated among natives and second-generation individuals; first-generation immigrants show little response to interviewer gender. One possible explanation is that social desirability pressure from a female interviewer operates through host-country social norms: Individuals more embedded in the Italian normative context are more responsive to cues from a female interviewer. These findings have implications for the design of surveys administered to mixed native-immigrant populations.
Cult of Personality or Lack of Competition? Determining Trump’s Sway Over Republican Partisans on a Critical Case
Love Christensen, Mattias Agerberg
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It is well established that partisans follow their leaders. While this potentially has important implications for political accountability, studies of opinion leadership rarely examine accountability directly. We test whether partisans are able to hold a dominant leader accountable under favorable conditions using a critical case: Trump’s position on the Epstein files. Two preregistered experiments, fielded before the release of the Epstein files, show that partisans follow Trump and do not hold him accountable when he is unopposed or contested by the Democrats. However, when Trump faces opposition from within his own bloc—especially countermessaging from right-coded media elites—both issue-specific and overall approval decline substantially, and following is halved. Our results have two important implications. First, opinion leadership does not imply the absence of political accountability. Second, meaningful political competition in polarized societies may need to originate from within ideological blocs. While Trump is powerful, credible opposition can make him accountable.

Social Science Computer Review

Pathways Out of Cybercrime: Perceived Desistance From Criminal Hacking
E. Rutger Leukfeldt, Luuk M. J. Bekkers
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The present study seeks to understand how criminal hackers self-report to desist from the world of cybercrime. In doing so, we address significant gaps in criminological literature, as little is known about pathways out of cybercrime offending. Interviews were conducted with 22 individuals involved in cyber-dependent crime, primarily from the Netherlands. Of these, 15 respondents were justice-involved youth and 7 respondents self-identified as criminal hackers. The interviews offer unique insights into the stages offenders navigate following their involvement in cybercrime. Findings reveal that young hackers may be susceptible to behavioral change through various mechanisms. Desistance from cybercrime is a process that warrants a multidisciplinary perspective, influenced particularly by social bonds, prosocial daily routines, and deterrent experiences. A combination of police intervention and alternative approaches that emphasize ethical application of skills may be a promising route for young hackers in particular, although actual intervention effects remain unverified. We encourage evaluations of current programs for hackers as well as additional quantitative assessments of protective factors among cybercrime offenders.
The Cost of Capturing: How Smartphone Photo-Taking Alters Memory Performance and Cognitive Self-Esteem: The Role of Smartphone Self-Expansion
Tianke Che, Wenxin Xu, Xiaochun Xie
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Taking photos to manage memories with smartphones is a routine strategy in the digital era. However, it remains unclear how smartphone photo-taking affects memory performance and self-evaluation. This research examined whether smartphone photo-taking is associated with a pattern consistent with the “Google effect,” and whether these associations vary with self-expansion via smartphone use. Across three experiments ( N = 363), participants photographed or observed materials before completing memory tests and measures of cognitive self-esteem. Study 1 tested the basic photo-taking effect; Study 2a examined device ownership as a contextual proxy for smartphone self-expansion; and Study 2b examined trait self-expansion as a continuous moderator. Results showed a photo-taking-impairment effect on memory, alongside higher cognitive self-esteem following photo-taking. Moreover, higher smartphone self-expansion was associated with stronger increases in cognitive self-esteem and greater memory overestimation, whereas its role in objective memory impairment remained inconclusive. Altogether, the findings suggest that smartphone photo-taking can reduce memory performance while increasing cognitive self-esteem.
Agreement Helps, Disagreement Matters: Scaling Framing Function Annotation With Large Language Models
Emily Robinson, Ranadheer Malla, Chico Q. Camargo
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Framing research has long relied on human annotation, but many aspects of framing – especially framing functions such as defining problems, identifying causes, making moral evaluations, or proposing solutions are difficult to label consistently and at scale. We demonstrate that large language models can reliably identify these framing functions, achieving levels of agreement comparable to those of human annotators. We evaluate three state-of-the-art models (GPT-5, Claude 3.7, and DeepSeek) alongside three trained human coders on a new dataset of social media posts about electric-vehicles, a highly topical and contested issue that holds many interpretations. This setting provides a strong test of both agreement and meaningful disagreement in framing-function annotation. Across models and humans, we find not only substantial convergence but also patterns of valid disagreement – differences that reflect legitimate alternative interpretations rather than annotation error. Recognising and preserving these cases enhances construct validity and provides a more realistic portrayal of how framing functions operate in contested debates. Our results demonstrate that large language models can scale framing function analysis while capturing the variability inherent in human interpretation, enabling forms of communication research that were previously impractical with human-only coding.