Search results

Abstract
This paper analyzes the changes Americans perceive to be taking place in their local weather and tests a series of hypotheses about why they hold these perceptions. Using data from annual nationwide surveys of the American public taken from 2008 to 2011, coupled with geographically specific measures of temperature and precipitation changes over that same period, the authors evaluate the relationship between perceptions of weather changes and actual changes in local weather. In addition, the survey data include measures of individual-level characteristics (age, education level, gender, and income) as well as cultural worldview and political ideology. Rival hypotheses about the origins of Americans' perceptions of weather change are tested, and it is found that actual weather changes are less predictive of perceived changes in local temperatures, but better predictors of perceived flooding and droughts. Cultural biases and political ideology also shape perceptions of changes in local weather. Overall, the analysis herein indicates that beliefs about changes in local temperatures have been more heavily politicized than is true for beliefs about local precipitation patterns. Therefore, risk communications linking changes in local patterns of precipitation to broader changes in the climate are more likely to penetrate identity-protective cognitions about climate.

AbstractEffective communication about severe weather requires that providers of weather information disseminate accurate and timely messages and that the intended recipients (i.e., the population at risk) receive and react to these messages. This article contributes to extant research on the second half of this equation by introducing a "real time" measure of public attention to severe weather risk communication based on the growing stream of data that individuals publish on social media platforms, in this case, Twitter. The authors develop a metric that tracks temporal fluctuations in tornado-related Twitter activity between 25 April 2012 and 11 November 2012 and assess the validity of the metric by systematically comparing fluctuations in Twitter activity to the issuance of tornado watches and warnings, which represent basic but important forms of communication designed to elicit, and therefore correlate with, public attention. The assessment finds that the measure demonstrates a high degree of convergent validity, suggesting that social media data can be used to advance our understanding of the relationship between risk communication, attention, and public reactions to severe weather.

ObjectiveSocial scientists from a variety of disciplines have employed concepts drawn from cultural theory (CT) to explain preferences across an array of issues. Recent research has challenged key elements of CT in a number of ways, perhaps most importantly by arguing that cultural types are simply another formulation of political ideology, and that only politically knowledgeable respondents reliably utilize either cultural or ideological categories in formulating preferences. This study reconsiders and expands upon this contention.MethodsPrincipal component analyses of responses to a U.S. national survey of 4,387 people.ResultsOur findings are threefold: (1) people with low levels of political knowledge are able to sort egalitarianism and individualism into coherent worldviews; (2) people with high levels of knowledge do not collapse egalitarianism and individualism onto a single scale of political ideology; and (3) regardless of levels of knowledge, survey respondents are able to recognize all four of the value orientations proposed by CT.ConclusionCT, which is related to but different than political ideology, offers a robust system of worldviews that both high‐ and low‐knowledge individuals might draw upon to formulate opinions and make decisions.

AbstractWeather and climate disasters pose an increasing risk to life and property in the United States. Managing this risk requires objective information about the nature of the threat and subjective information about how people perceive it. Meteorologists and climatologists have a relatively firm grasp of the historical objective risk. For example, we know which parts of the United States are most likely to experience drought, heat waves, flooding, snow or ice storms, tornadoes, and hurricanes. We know less about the geographic distribution of the perceived risks of meteorological events and trends. Do subjective perceptions align with exposure to weather risks? This question is difficult to answer because analysts have yet to develop a comprehensive and spatially consistent methodology for measuring risk perceptions across geographic areas in the United States. In this project, we propose a methodology that uses multilevel regression and poststratification to estimate extreme weather and climate risk perceptions by geographic area (i.e., region, state, forecast area, and county). Then we apply the methodology using data from three national surveys (n = 9,542). This enables us to measure, map, and compare perceptions of risk from multiple weather hazards in geographic areas across the country.

AbstractTornadoes impose enormous costs on society. Relatively simple and inexpensive enhancements to building codes may reduce these costs by 30% or more, but only one city in the United States has adopted these codes. Why is this the case? This analysis addresses this question by examining homeowner support for more stringent building codes in Oklahoma, a conservative state that routinely experiences damaging tornadoes. Survey data show that support for mandatory mitigation policies like building codes is subject to countervailing forces. Push dynamics, including objective risk data, homeowners' risk perceptions, and damage experience, encourage support for mitigation. Pull dynamics, such as individualistic and conservative worldviews, and skepticism about climate change, generate opposition. At the margin, the pull dynamics appear to exert more force than push dynamics, creating only a weak basis of support that is not strong enough to overcome the status quo bias in a state that is cautious about regulatory measures. The concluding section offers suggestions for changing these dynamics.

Theory and conventional wisdom suggest that errors undermine the credibility of tornado warning systems and thus decrease the probability that individuals will comply (i.e., engage in protective action) when future warnings are issued. Unfortunately, empirical research on the influence of warning system accuracy on public responses to tornado warnings is incomplete and inconclusive. This study adds to existing research by analyzing two sets of relationships. First, we assess the relationship between perceptions of accuracy, credibility, and warning response. Using data collected via a large regional survey, we find that trust in the National Weather Service (NWS; the agency responsible for issuing tornado warnings) increases the likelihood that an individual will opt for protective action when responding to a hypothetical warning. More importantly, we find that subjective perceptions of warning system accuracy are, as theory suggests, systematically related to trust in the NWS and (by extension) stated responses to future warnings. The second half of the study matches survey data against NWS warning and event archives to investigate a critical follow‐up question—Why do some people perceive that their warning system is accurate, whereas others perceive that their system is error prone? We find that subjective perceptions are—in part—a function of objective experience, knowledge, and demographic characteristics. When considered in tandem, these findings support the proposition that errors influence perceptions about the accuracy of warning systems, which in turn impact the credibility that people assign to information provided by systems and, ultimately, public decisions about how to respond when warnings are issued.

The focus of this book is on using quantitative research methods to test hypotheses and build theory in political science, public policy and public administration. It is designed for advanced undergraduate courses, or introductory and intermediate graduate-level courses. The first part of the book introduces the scientific method, then covers research design, measurement, descriptive statistics, probability, inference, and basic measures of association. The second part of the book covers bivariate and multiple linear regression using the ordinary least squares, the calculus and matrix algebra that are necessary for understanding bivariate and multiple linear regression, the assumptions that underlie these methods, and then provides a short introduction to generalized linear models. The book fully embraces the open access and open source philosophies. The book is freely available in the SHAREOK repository; it is written in R Markdown files that are available in a public GitHub repository; it uses and teaches R and RStudio for data analysis, visualization and data management; and it uses publically available survey data (from the Meso-Scale Integrated Socio-geographic Network) to illustrate important concepts and methods. We encourage students to download the data, replicate the examples, and explore further! We also encourage instructors to download the R Markdown files and modify the text for use in different courses.

AbstractAs the United States is at historic lows of trust in government, various agencies are seeking to convince members of the public to take key protective actions and to support novel policy actions intended to reduce the spread of COVID‐19. This article assesses the status of trust in key organizations relevant to pandemic information based on a national survey of residents of the United States. First, the article illustrates the variations in trust placed in various agencies—local to global agencies and governmental and private sector organizations. Second, the analysis reveals considerable variances in trust in specific organizations based on party identification. Finally, the results indicate that trust is important as it is related to the intention to adopt personal protective actions and support for key public policies.

Survey-based contingent valuation (CV) techniques are commonly used to value the potential effects of a policy change when market-based valuation of those effects is not possible. The results of these analyses are often intended to inform policy decisions, which are made within the context of formal policymaking institutions. These institutions are typically designed to reduce the large number of potential options for addressing any given policy problem to a binary choice between the continuation of current policy and a single, specified alternative. In this research we develop an approach for conducting CV exercises in a manner consistent with the decision structure typically faced by policymakers. The data generated from this approach allow for an estimate of willingness to pay (WTP) for a defined policy alternative, relative to leaving policy unchanged, which we argue is of direct interest to policymakers. We illustrate our approach within the context of policy governing the storage of used nuclear fuel in the United States. We value the policy option of constructing an interim storage facility relative to continuation of current policy, wherein used nuclear fuel is stored on-site at or near commercial nuclear generating plants. We close the paper with a discussion of the implications for future research and the role of CV in the policymaking process.