Concept: Opinion poll
Political candidates often believe they must focus their campaign efforts on a small number of swing voters open for ideological change. Based on the wisdom of opinion polls, this might seem like a good idea. But do most voters really hold their political attitudes so firmly that they are unreceptive to persuasion? We tested this premise during the most recent general election in Sweden, in which a left- and a right-wing coalition were locked in a close race. We asked our participants to state their voter intention, and presented them with a political survey of wedge issues between the two coalitions. Using a sleight-of-hand we then altered their replies to place them in the opposite political camp, and invited them to reason about their attitudes on the manipulated issues. Finally, we summarized their survey score, and asked for their voter intention again. The results showed that no more than 22% of the manipulated replies were detected, and that a full 92% of the participants accepted and endorsed our altered political survey score. Furthermore, the final voter intention question indicated that as many as 48% (±9.2%) were willing to consider a left-right coalition shift. This can be contrasted with the established polls tracking the Swedish election, which registered maximally 10% voters open for a swing. Our results indicate that political attitudes and partisan divisions can be far more flexible than what is assumed by the polls, and that people can reason about the factual issues of the campaign with considerable openness to change.
Polls examining public opinion on the subject of climate change are now commonplace, and one-off public opinion polls provide a snapshot of citizen’s opinions that can inform policy and communication strategies. However, cross-sectional polls do not track opinions over time, thus making it impossible to ascertain whether key climate change beliefs held by the same group of individuals are changing or not. Here we examine the extent to which individual’s level of agreement with two key beliefs (“climate change is real” and “climate change is caused by humans”) remain stable or increase/decrease over a six-year period in New Zealand using latent growth curve modelling (n = 10,436). Data were drawn from the New Zealand Attitudes and Values Study, a probabilistic national panel study, and indicated that levels of agreement to both beliefs have steadily increased over the 2009-2015 period. Given that climate change beliefs and concerns are key predictors of climate change action, our findings suggest that a combination of targeted endeavors, as well as serendipitous events, may successfully convey the emergency of the issue.
While the majority of veteran suicides involve firearms, no contemporary data describing firearm ownership among US veterans are available. This study uses survey data to describe the prevalence of firearm ownership among a nationally representative sample of veterans, as well as veterans' reasons for firearm ownership.
The consequences of anthropogenic climate change are extensively debated through scientific papers, newspaper articles, and blogs. Newspaper articles may lack accuracy, while the severity of findings in scientific papers may be too opaque for the public to understand. Social media, however, is a forum where individuals of diverse backgrounds can share their thoughts and opinions. As consumption shifts from old media to new, Twitter has become a valuable resource for analyzing current events and headline news. In this research, we analyze tweets containing the word “climate” collected between September 2008 and July 2014. Through use of a previously developed sentiment measurement tool called the Hedonometer, we determine how collective sentiment varies in response to climate change news, events, and natural disasters. We find that natural disasters, climate bills, and oil-drilling can contribute to a decrease in happiness while climate rallies, a book release, and a green ideas contest can contribute to an increase in happiness. Words uncovered by our analysis suggest that responses to climate change news are predominately from climate change activists rather than climate change deniers, indicating that Twitter is a valuable resource for the spread of climate change awareness.
Purpose . We aimed to understand how employer characteristics relate to the use of incentives to promote participation in wellness programs and to explore the relationship between incentive type and participation rates. Design . A cross-sectional analysis of nationally representative survey data combined with an administrative business database was employed. Settings/Subjects . Random sampling of U.S. companies within strata based on industry and number of employees was used to determine a final sample of 3000 companies. Of these, 19% returned completed surveys. Measures . The survey asked about employee participation rate, incentive type, and gender composition of employees. Incentive types included any incentives, high-value rewards, and rewards plus penalties. Analysis . Logistic regressions of incentive type on employer characteristics were used to determine what types of employers are more likely to offer which type of incentives. A generalized linear model of participation rate was used to determine the relationship between incentive type and participation. Results . Employers located in the Northeast were 5 to 10 times more likely to offer incentives. Employers with a large number of employees, particularly female employees, were up to 1.25 times more likely to use penalties. Penalty and high-value incentives were associated with participation rates of 68% and 52%, respectively. Conclusion . Industry or regional characteristics are likely determinants of incentive use for wellness programs. Penalties appear to be effective, but attention should be paid to what types of employees they affect.
In October, we reported on voters' views of health care and how those views might influence their choices in the 2012 election.(1) Now that the election is over, we have analyzed a range of pre-election and post-election polls as part of a Robert Wood Johnson Foundation project. The data are derived from three types of polls. The first is a 2012 national exit poll, comprising the responses of 26,565 voters as they exited voting places and those from 4408 telephone interviews (landline and cell phone) with early and absentee voters. The second are 11 pre-election polls conducted by telephone (landline . . .
Traditional metrics of the impact of the Affordable Care Act (ACA) and health insurance marketplaces in the United States include public opinion polls and marketplace enrollment, which are published with a lag of weeks to months. In this rapidly changing environment, a real-time barometer of public opinion with a mechanism to identify emerging issues would be valuable.
Household survey data are collected by governments, international organizations, and companies to prioritize policies and allocate billions of dollars. Surveys are typically selected from recent census data; however, census data are often outdated or inaccurate. This paper describes how gridded population data might instead be used as a sample frame, and introduces the R GridSample algorithm for selecting primary sampling units (PSU) for complex household surveys with gridded population data. With a gridded population dataset and geographic boundary of the study area, GridSample allows a two-step process to sample “seed” cells with probability proportionate to estimated population size, then “grows” PSUs until a minimum population is achieved in each PSU. The algorithm permits stratification and oversampling of urban or rural areas. The approximately uniform size and shape of grid cells allows for spatial oversampling, not possible in typical surveys, possibly improving small area estimates with survey results.
While it is estimated that 15% of couples worldwide are infertile, this figure hinges critically on the quality, inclusiveness and availability of infertility data sources. Current infertility data and statistics fail to account for the infertility experiences of some social groups. We identify these people as the invisible infertile , and refer to their omission from infertility data and statistics-whether intentional or unintentional-as the process of invisibilization . We identify two processes through which invisibilization in survey data is produced: sampling, with focus on exclusionary definitions of the population at-risk, and survey instrument design, with focus on skip patterns and question wording. Illustrative examples of these processes are drawn from the Integrated Fertility Survey Series and the Demographic and Health Surveys. Empirical research is not designed in an objective vacuum. Rather, survey instruments and sampling techniques are shaped and influenced by the sociocultural norms and geopolitical context of the time and place in which they are created and conducted, reflecting broader social beliefs about family building and reproduction. Furthermore, population policy singularly aimed at curbing overpopulation in high fertility parts of the world limits the type of reproduction data collected, effectively rendering the infertility of some groups epidemiologically unfathomable. In light of these sociocultural and geopolitical forces, many marginalized groups are missing from reproductive health (RH) statistics. The omission of entire groups from the scientific discourse casts doubt on the quality of research questions, validity of the analytic tools, and accuracy of scientific findings. Invisibility may also misguide evidence-based RH and family planning policies and deter equitable access to reproductive healthcare for some social groups, perpetuating social inequalities.
Data fraud and selective reporting both present serious threats to the credibility of science. However, there remains considerable disagreement among scientists about how best to sanction data fraud, and about the ethicality of selective reporting. The public is arguably the largest stakeholder in the reproducibility of science; research is primarily paid for with public funds, and flawed science threatens the public’s welfare. Members of the public are able to make meaningful judgments about the morality of different behaviors using moral intuitions. Legal scholars emphasize that to maintain legitimacy, social control policies must be developed with some consideration given to the public’s moral intuitions. Although there is a large literature on popular attitudes toward science, there is no existing evidence about public opinion on data fraud or selective reporting. We conducted two studies-a survey experiment with a nationwide convenience sample (N = 821), and a follow-up survey with a representative sample of US adults (N = 964)-to explore community members' judgments about the morality of data fraud and selective reporting in science. The findings show that community members make a moral distinction between data fraud and selective reporting, but overwhelmingly judge both behaviors to be immoral and deserving of punishment. Community members believe that scientists who commit data fraud or selective reporting should be fired and banned from receiving funding. For data fraud, most Americans support criminal penalties. Results from an ordered logistic regression analysis reveal few demographic and no significant partisan differences in punitiveness toward data fraud.