Integrating Risk Perception Measures, Antecedents, and Outcomes

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Risk perception has been central to risk analysis since it began, but how we define and measure “risk perception” can greatly affect scholars’ grasp of its sources or outcomes. Unfortunately, use of risk perception measures has been unsystematic, within and across multiple fields (e.g., health behavior). Strikingly, four critical topics—how to measure risk perception; how risk perception shapes outcomes such as behavioral intentions and policy support; and sources of risk perceptions—usually are studied separately, despite obvious mutual implications: e.g., whether and how risk perceptions are affected by, or affect, other variables might depend on how they are measured. Most quantitative research on these topics has used cross-sectional survey designs, inadequate to test most critical hypotheses. Temporal changes are better tested with longitudinal panel designs, but even when (rarely) used, far more attention is paid to the observed sign of the association than to whether its magnitude or sign varies over time. We also know little about whether or how these associations vary across hazards. By more systematically assessing associations across multiple perception measures, hazards, and time, the findings about similarities and differences can improve risk communication and risk management efforts. These improvements will reduce harms to humans and what they value. The proposed research uses focus groups and U.S. nationally representative cross-sectional and longitudinal panel surveys. This first systematic attempt to simultaneously model risk perception measures, antecedents, and outcomes (behavioral intentions, policy support) offers a chance to integrate a field splintered across these four related topics and multiple scholarly disciplines. This effort aims to provide a foundation that can be extended to both specific fields and topic areas, and potentially to other societies beyond the U.S. data generated in this research project. The new knowledge is about the degree to which risk perception patterns generalize across humans and situations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.