Enhancing a Data Set for Use in Structural Modeling of Risk Perception : Doctoral Dissertation

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This dissertation enhancement proposal outlines an approach to understanding factors underlying the risk amplification process and the ways in which expert opinions on risk perception combine with views of the public. Although the influence of risk perceptions on public policy has been a popular topic for researchers over the past decade, most of the analytical work has employed a small set of methods (e.g., factor analysis) and has emphasized descriptive rather than prescriptive elements of the attitude formation process. The proposed research will attempt to use alternative data- interpretation methods, such as partial least squares and covariance structure models, to review existing data sets; these methods are thought to provide a stronger basis for developing general structural models that could provide useful theoretical insights. In addition, the project will collect additional data from an applied setting that will be used to test alternative hypotheses about the risk amplification process.