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

这篇论文的增强建议概述了一种方法 了解风险放大的潜在因素 专家对风险的意见 感知联合收割机与公众的观点相结合。 虽然 风险认知对公共政策的影响一直是 在过去的十年里，研究人员的热门话题，大多数 分析工作采用了一小套方法， (e.g.,因素分析），并强调描述性，而不是 而不是态度形成过程中的规定性因素。 拟议的研究将尝试使用替代数据- 解释方法，如偏最小二乘法和 协方差结构模型，审查现有数据集; 这些方法被认为为以下方面提供了更有力的基础： 开发通用结构模型， 有用的理论见解。 此外，该项目将 从应用的设置中收集其他数据， 用于检验关于风险的替代假设 放大过程

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.