Multimethod Mediation Analysis in Prevention Research

描述（由申请人提供）：预防领域在很大程度上依赖于理解因果过程，作为确定潜在预防目标以及干预措施如何发挥作用以实现其效果的一种方式。统计中介分析是预防研究的重要工具，因为它有助于解释自变量如何对因变量产生影响。此外，使用多种方法和/或多个评分者来评估预防科学中感兴趣的结构是非常有价值的，因为多方法研究比单一方法设计更具信息性，并且允许评估收敛效度和方法特异性。尽管最近的许多研究使用多方法测量设计来研究中介效应，但许多用于在统计分析中整合多种方法的方法具有显著的理论和经验局限性。目前的研究旨在解决这个问题，通过整合现代统计中介分析方法与现代方法的多特征多方法（MTMM）方法。特别是，我们建议1）检查预防科学家目前使用的方法的相对统计性能（目标1）和2）开发和评估新的多方法中介模型，其中包含适当考虑研究中使用的方法类型的潜在变量（目标2）。与Eid等人（2008年）一致，我们在这方面区分了可互换和结构不同的方法，并建议为每种方法以及两者的组合开发模型。模拟研究将用于评估新模型的绝对性能以及与其他既定方法的关系。根据我们在目标1和目标2中的模拟研究结果，我们将把性能最好的MM中介模型应用于真实的预防数据集（目标3）。最后，本研究的最终目标是传播知识的应用研究人员如何最适当地分析多方法测量设计的背景下，中介效应（目标4）。成功实现本项目提出的目标将对公共卫生产生影响，因为它将有助于澄清预防中的中介作用的含义 这是设计有效预防干预措施的一个关键要素。

DESCRIPTION (provided by applicant): The field of prevention relies heavily on understanding causal processes as a way of identifying potential targets for prevention and how interventions operate to achieve their effects. Statistical mediation analysis is a critical tool fr prevention research because it helps explain how an independent variable exerts its effect on a dependent variable. Furthermore, the use of multiple methods and/or multiple raters to assess the constructs of interest in prevention science is greatly valued, because multimethod studies are more informative than single method designs and allow for the assessment of convergent validity and method specificity. Despite the fact that many recent studies have used multi-method measurement designs to study mediated effects, many of the approaches used to integrate multiple methods in the statistical analyses have significant theoretical and empirical limitations. The current research aims to address this issue by integrating modern methods of statistical mediation analysis with modern approaches of multitrait-multimethod (MTMM) methodology. In particular, we propose to 1) examine the relative statistical performance of approaches currently used by prevention scientists (Aim 1) and 2) develop and evaluate new multimethod mediation models with latent variables that properly account for the types of methods used in the study (Aim 2). In line with Eid et al. (2008), we distinguish between interchangeable and structurally different methods in this regard and propose to develop models for each type of method as well as the combination of both. Simulation studies will be used to evaluate the performance of the new models in absolute terms as well as in relation to other, already established approaches. Based on our findings from the simulation studies in Aim 1 and Aim 2, we will apply the best performing MM mediation models to real prevention datasets (Aim 3). Finally, the ultimate goal of this research is to disseminate knowledge to applied researchers about how to most appropriately analyze mediated effects in the context of a multimethod measurement design (Aim 4). The successful fulfillment of the aims proposed in this project will impact public health because it will help to clarify the meaning of mediating effects in prevention studies, which is a critical element in designing effective preventive interventions.