I checked 7 public opinion journals on Thursday, August 13, 2026 using the Crossref API. For the period August 06 to August 12, I found 8 new paper(s) in 4 journal(s).

Journal of Elections, Public Opinion and Parties

Support for executive aggrandizement under weak polarization
Ikuma Ogura, Hirofumi Miwa, Yuko Kasuya
Full text
Valence strategies and polling support during electoral campaigns
Stefano Sangiovanni
Full text

Journal of Official Statistics

A Factorization of Means of Order r and Its Applications to Price Indices
Hiroshi Saigo
Full text
This article presents a factorization of means of order r , which provides a method for numerical comparison among standard index numbers. We apply it to a family of superlative indices written by the quadratic mean of order r , elementary indices (the Carli, Dutot, and Jevons indices), and the Lowe, Young, and related indices. Descriptive measures are provided to assess the differences between index numbers. Empirical data are studied and discussed to evaluate the theoretical results of the proposed factorization.
Dual System Estimation Using Mixed Effects Loglinear Models
Ceejay Hammond, Paul A. Smith, Peter G. M. van der Heijden
Full text
In official statistics, dual system estimation (DSE) is a well-known tool to estimate the size of a population. Two sources are linked, and the number of units that are missed by both sources is estimated. Often dual system estimation is carried out in each of the levels of a stratifying variable, such as region. DSE can be considered a loglinear independence model, and, with a stratifying variable, a loglinear conditional independence model. The standard approach is to estimate parameters for each level of the stratifying variable. Thus, when the number of levels of the stratifying variable is large, the number of parameters estimated is large as well. Mixed effects loglinear models, where sets of parameters involving the stratifying variable are replaced by a distribution parameterised by its mean and a variance, have also been proposed, and we investigate their properties through simulation. In our simulation studies, the mixed effects loglinear model outperforms the fixed effects loglinear model, although only to a small extent in terms of mean squared error. We show how mixed effects dual system estimation can be extended to multiple system estimation.

Journal of Survey Statistics and Methodology

Youth Nonresponse in Panel Surveys: Investigating the Impact of Life Events
Camilla Salvatore, Peter Lugtig, Bella Struminskaya
Full text
Nonresponse in surveys is particularly problematic among young people in both cross-sectional and panel studies. This article investigates the factors driving lower participation rates among young people in longitudinal surveys. We study whether nonresponse can be explained by young people experiencing more life events associated with disengagement from panel surveys. Using data from the Understanding Society panel in the United Kingdom, we employ a discrete-time multinomial logistic hazard model to study nonresponse across panel waves. Consistent with previous research, our analysis identifies lower education, unemployment, immigrant background, and residential circumstances as key predictors of nonresponse. Furthermore, we demonstrate that changes in employment status and (expectations of) residential relocation significantly contribute to predicting attrition among young participants, with age remaining a significant factor. Living with parents also plays an important role, as it is associated with a lower risk of non-contact.
Varying the Content of Political Science Surveys to Improve the Survey Experience and Data Quality of Politically Disengaged Respondents
Saskia Bartholomäus, Tobias Gummer
Full text
Respondents’ survey experience affects their answering and participation behavior and, consequently, measurement and nonresponse errors. Political science surveys especially suffer from biased estimates of political attitudes and behavior. Improving respondents’ survey experience by offering a more interesting and varied questionnaire poses a possible solution to this problem. In this regard, we break new ground by investigating whether improving politically disengaged respondents’ survey experience by including non-political survey content enhances data quality with respect to measurement and nonresponse errors in political science surveys. For this purpose, we conducted two survey experiments in a probability- and a nonprobability-based panel that varied the content of a political science questionnaire. This study provides the first evidence that offering a varied questionnaire rather than a purely political one lowered or even diminished the gap in measurement and nonresponse errors between politically engaged and disengaged respondents.

Public Opinion Quarterly

AAPOR Presidential Address AAPOR Presidential Address 2026: Bearing Witness to This Moment
Jordon Peugh
Full text
Assessing Nonignorable Response: Sensitivity Analysis for Survey Weighting, with Applications to Survey Estimates of COVID-19 Vaccination Uptake
Melody Huang, Erin Hartman
Full text
Standard statistical uncertainty measures, like standard errors, are unable to quantify the uncertainty from an unrepresentative sampling process. Existing work has highlighted the potential risk in large datasets, in which larger surveys resulting in more precise estimates can misleadingly overstate the degree of confidence in biased survey results. In this research note, we introduce a sensitivity framework for researchers to transparently summarize their uncertainty to a biased sampling process. We illustrate the framework on a set of COVID-19 vaccine surveys, and show that had researchers conducted a sensitivity analysis, they would have found that despite substantially smaller standard errors, the larger surveys conducted on less representative samples were more susceptible to potential bias than smaller surveys conducted on more representative samples. We argue that these summary measures should be routinely reported to accurately quantify uncertainty to potential bias, allowing researchers to assess their confidence in results, in large and small datasets.