I checked 7 public opinion journals on Wednesday, August 05, 2026 using the Crossref API. For the period July 29 to August 04, I found 9 new paper(s) in 5 journal(s).

Journal of Elections, Public Opinion and Parties

Pandemic politics: electoral outcomes for incumbents during COVID-19
Jacek Lewkowicz, Jan Fałkowski, Eliza Hałatek
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Varieties of anti-establishment appeals: valence, ideology, and support for outsiders
Lautaro Cella
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Place resentment in England: measurement and effects
Kal Munis, Joseph Phillips, Brendan Szendrő
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Emotional reactions to referendum campaigns: how news consumption and political orientations made Chileans feel about the constitutional process
Ximena Orchard, Magdalena Saldaña, Daniela Lazcano-Peña
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Journal of Official Statistics

Applying Multiple Imputation to Improve Estimates from Web Surveys: An Alternative to the Pseudoweighting Method
Yulei He, Katherine Irimata, Guangyu Zhang, Yan Li
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To improve the timeliness of data products, survey researchers and practitioners have increasingly used web surveys and alike to collect information for population health research and dissemination. From the statistical inferential perspective, these data are often referred to as nonprobability samples due to the lack of a well-defined probability sampling structure, or they come from probability panel surveys yet are subject to high nonresponse and/or coverage errors. Certain statistical adjustments are therefore needed to make proper inferences using web surveys. With a high-quality reference probability survey available, one popular adjustment approach is to create pseudoweights that properly “weight” the web survey samples back to the target population underlying the reference survey in order to produce population-weighted estimates of the target of interest. When the variable of interest is collected in the web survey but not in the reference survey, the analytical question can also be framed as a missing data problem. Thus we propose to multiply impute the missing variable on the reference survey which combines the information from both data sources. We illustrate main features and performances of the multiple imputation strategy using a simulation study. The simulation results have shown that the multiple imputation analysis method is comparable to the pseudoweighting method, both of which can adequately account for the nonprobability feature of web surveys to improve the statistical inference. We also present a real data analysis based on the web-based Research and Development Survey and the interviewer-administered National Health Interview Survey. Our research shows that results from different imputation and pseudoweighting models can be compared to better understand features of the web survey data and improve the analysis.

Journal of Survey Statistics and Methodology

Three-fold Fay-Herriot model with unequal variance random effects
Esteban Cabello, Laura Marcis, Domingo Morales, Maria Chiara Pagliarella, Renato Salvatore
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This study extends the classical Fay–Herriot model to a threefold hierarchical structure incorporating heteroscedastic random effects. Three submodels are also introduced within this framework. Small area best linear unbiased predictors are derived for linear indicators, and their mean squared errors (MSEs) are estimated using both analytical and parametric bootstrap methods. Model performance and reliability are evaluated through diagnostic tools and influence measures, specifically designed for small area estimation. Simulation experiments are conducted to analyze the empirical properties of the predictors and MSE estimators. The proposed approach is applied to data from the 2019–2021 Spanish Living Conditions Survey to estimate the proportion of women and men below the poverty line, disaggregated by province, age group, and year.
Mode Effects on Regression Coefficients Capturing Subgroup Differences: Comparing Regression Models Across the Web-Based American Family Health Study and the Face-to-Face National Survey of Family Growth
Sihle Khanyile, Shiyu Zhang, Brady T West, William G Axinn
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This article investigates differences between self-administered and interviewer-administered modes in population inferences related to important associations between predictors and outcomes that capture subgroup differences. The study focuses on measures of personal experiences with a range of privacy considerations, from more private measures of sexual partnerships and contraceptive use to less private measures of cohabitation and marriage. We compare the self-administered web/mail approach used by the American Family Health Study (AFHS, 2020–2022) to the interviewer-administered data collection used by the National Survey of Family Growth (2017–2019). The existing mode effects literature suggests that about 20 percent of estimated regression coefficients are moderated by survey mode, and these differences are more likely for sensitive, private outcomes. We investigate multivariable models for these four outcomes, separately for male and female respondents, based on these two nationally representative surveys (AFHS and NSFG) that used the exact same question wording but different survey modes. We test models including the most common predictors in the literature on these outcomes (age, race and ethnicity, and level of education). We compare results, ignoring study-specific weights based on the respective sample design features and then when employing the weights for estimation. We find few significant differences in inference between these two national studies, suggesting that the more cost-efficient AFHS approach can effectively replicate population inferences about important family and reproductive health outcomes.

Public Opinion Quarterly

Nonpolitical Identity Signals as Information Shortcuts: Evidence from Three Experiments
Levi G Allen, Wayde Z C Marsh
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A rich literature has documented how voters navigate the complex information landscape by using heuristics, or “information shortcuts,” to help them make sense of the political world. Additionally, a budding line of research argues that our current polarization crisis has extended beyond just the political and now even affects the nonpolitical realm. We submit that voters may be using these nonpolitical identity signals (e.g., a candidate driving a Toyota Prius or eating at Chick-fil-A) as heuristics to make sense of the contemporary political era. Using three original survey experiments, we find that these nonpolitical identity cues are indeed used by voters as partisan heuristics, but only in the absence of political (partisan and issue) informational cues. Furthermore, we also suppress the effect of partisan identification when we provide orthogonal nonpolitical information. These findings show that nonpolitical identity items can be used by citizens as information shortcuts to help citizens navigate the complicated political waters that surround them, but that their effect in light of other pieces of information is minimal.

Social Science Computer Review

Where You Are Is What You Get! Inconsistencies of Digital Trace Data across Download Locations
Johanna Hölzl, Florian Keusch, John Collins
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To collect digital trace data, researchers continue to rely on for-profit companies’ Application Programming Interfaces (APIs). These APIs often return samples of the data based on intransparent sampling procedures and algorithms. In this paper, we extend research on the reliability of digital trace data from APIs by examining the effect of the download location on inconsistencies across returned samples: Do we get different values from digital trace data APIs depending on where we download the data from? We compare samples from Google Trends, YouTube Data, and the New York Times (NYT) API from four countries across three continents (Austria, Germany, the U.S., and Australia) for the same query parameters (i.e., search term, region, and time range). Our results show that the download location impacts the returned samples for all three APIs, depending on the query. We find large inconsistencies for samples from Google Trends and the YouTube Data API, while the NYT API returns identical article sets from each download location for most queries. We conclude with practical recommendations for researchers using these APIs. Our findings serve as a cautionary reminder for social scientists relying on sampling-based APIs as they point to yet another limitation regarding their reliability and reproducibility.