Lessons From 12 Years of Comparative Risk Projects

Jones, Ken; Klein, Heidi

doi:10.1146/annurev.publhealth.20.1.159

Cited by 7 publications

(6 citation statements)

References 4 publications

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“…For comparative risk analysis (e.g., Jones and Klien, 1999;Minard, 1996) and risk ranking (e.g., DeKay et al, 2001;Florig et al, 2001; M.G. Morgan et al, 1996;K.M.…”

Section: Crossing the Level And Focus Of Analysismentioning

confidence: 99%

Accounting for Variation in the Explanatory Power of the Psychometric Paradigm: The Effects of Aggregation and Focus

Bronfman

Cifuentes

DeKay

et al. 2007

Journal of Risk Research

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Most psychometric studies of risk perception have used data that have been averaged over participants prior to analysis. Such aggregation obscures variation among participants and inflates the magnitude of relationships between psychometric dimensions and dependent variables such as overall riskiness. However, most studies that have not averaged data over participants have also shifted the focus of analysis from differences among hazards to differences among participants. Hence, it is unclear whether observed reductions in the explanatory power of psychometric dimensions result from the change in the level of analysis or from the change in the focus of analysis. Following Willis et al.'s (2005) analysis of ecological risk perceptions, we unconfound these two variables in a study of risk perceptions in Santiago, Chile, although we use more traditional hazards, attributes, and statistical procedures. Results confirm that psychometric dimensions explain less variation in judgments of riskiness and acceptability at the disaggregate level than at the aggregate level. However, they also explain less variation when the focus of analysis is differences among participants rather than differences among hazards. These two effects appear to be similar in magnitude. A simple hybrid analysis economically represents variation among participants' judgments of hazards' riskiness by relating those judgments to a common set of psychometric dimensions from a traditional aggregate-level analysis.

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“…For comparative risk analysis (e.g., Jones and Klien, 1999;Minard, 1996) and risk ranking (e.g., DeKay et al, 2001;Florig et al, 2001; M.G. Morgan et al, 1996;K.M.…”

Section: Crossing the Level And Focus Of Analysismentioning

confidence: 99%

Accounting for Variation in the Explanatory Power of the Psychometric Paradigm: The Effects of Aggregation and Focus

Bronfman

Cifuentes

DeKay

et al. 2007

Journal of Risk Research

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“…Environmental Protection Agency (EPA), 1987, 1990, 2000; U.S. Occupational Safety and Health Administration, 1996), dozens of state and local‐level comparative risk projects have been completed (Jones & Klien, 1999; Minard, 1996). Risks have often been evaluated on the basis of human health endpoints (e.g., cancer and noncancer health effects); ecological endpoints (e.g., biodiversity, endangered species, reversibility); and quality‐of‐life endpoints (e.g., aesthetics, economics, fairness), as suggested by the U.S.…”

Section: Introductionmentioning

confidence: 99%

Aggregate, Disaggregate, and Hybrid Analyses of Ecological Risk Perceptions

et al. 2005

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Laypeople's perceptions of health and safety risks have been widely studied, but only a few studies have addressed perceptions of ecological hazards. We assembled a list of 39 attributes of ecological hazards from the literatures on comparative risk assessment, ecological health, environmental conservation and management, environmental psychology, and risk perception. In Study 1, 125 laypeople evaluated 83 hazards on subsets of this attribute set. Factor analysis of attribute ratings (averaged over participants) revealed six oblique factors: ecological impacts, human impacts, human benefits, aesthetic impacts, scientific understanding, and controllability. These factors predicted mean judgments of overall riskiness, ecological riskiness, acceptability, and regulatory strictness. In Study 2, 30 laypeople each evaluated 34 hazards on 17 attributes and 3 dependent variables. Aggregate-level factor analysis of these data replicated the appropriate portion of the factor solution and yielded similar regression results. Parallel analyses at the individual-participant level yielded factors that explained less variance in judgments of overall riskiness, ecological riskiness, and acceptability. However, the decrease in explanatory power was much less than is often reported for disaggregate-level analyses of psychometric data. This discrepancy illustrates the importance of distinguishing between the level of analysis (aggregate versus disaggregate) and the focus of analysis (distinctions among hazards versus distinctions among participants). In a hybrid analysis, aggregate-level factor scores predicted individual participants' riskiness judgments reasonably well. Psychometric studies such as these provide a sound empirical basis for selecting attributes of ecological hazards for use in comparative risk assessment.

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“…The table of 30 activities and technologies first developed by Slovic et al in 1980 5 (and reproduced in his influential 1987 paper in Science , 6 which in turn has been cited in at least 2000 other sources) shows, for example, that members of the League of Women Voters ranked nuclear power as the riskiest of the 30 hazards, while experts ranked it 20th; conversely, experts ranked medical X‐rays 15 places higher than the laypeople did. The exercise of ranking risks by perceived and “actual” severity has since found its way into numerous federal and state comparative risk projects, 7 which have often recommended to legislatures that agency budgets be reallocated so that they can focus on the “worst risks” rather than the ones the public most fears.…”

Section: Risk Perception Before Neurosciencementioning

confidence: 99%

Perceiving Others’ Perceptions of Risk

Finkel

2008

Annals of the New York Academy of Sciences

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The recent literature providing insights from neuroscience and evolutionary biology into how individuals perceive risky choice situations represents a "second wave" of findings that recapitulates as well as challenges the risk perception research begun in the 1980s, which relied on psychometric survey research. Gleaning insights from the first wave of research that could improve the communication and control of environmental risks has yielded disappointing results. This is a result, in part, of the eagerness of scholars and pundits to posit a chasm between the "rational" and "objective" perceptions of experts, on the one hand, and a lay public that is seen as lurching between "paranoia and neglect" and as insensitive to the magnitude of risks. Interpretations of the psychometric research have suffered from inattention to uncertainty and interindividual variability in risk, to expert biases, and to important aspects of risky choice that were not explored in the first wave of research. Initial signs indicate that neuroscience and evolutionary biology research may fall prey to similar misinterpretations. This article summarizes some of the most intriguing findings of the "second wave" of risk perception research and advances four themes that may help make the new findings less divisive and more useful for improving risk communication and risk management. Continued research into risk perception should perhaps be embedded in a more general theory of public choice in the face of uncertain and variable costs and benefits and with a respect for distributive justice as an important goal in risk management.

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Lessons From 12 Years of Comparative Risk Projects

Cited by 7 publications

References 4 publications

Accounting for Variation in the Explanatory Power of the Psychometric Paradigm: The Effects of Aggregation and Focus

Accounting for Variation in the Explanatory Power of the Psychometric Paradigm: The Effects of Aggregation and Focus

Aggregate, Disaggregate, and Hybrid Analyses of Ecological Risk Perceptions

Perceiving Others’ Perceptions of Risk

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