I checked 7 public opinion journals on Sunday, August 02, 2026 using the Crossref API. For the period July 26 to August 01, I found 6 new paper(s) in 4 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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Ticket-splitting in Georgia
David Cottrell, M.V. Hood III, Seth C. McKee, Daniel A. Smith
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Place resentment in England: measurement and effects
Kal Munis, Joseph Phillips, Brendan Szendrő
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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.

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.