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和干预措施如何运作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提供更多信息，并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了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着的理论和经验局限性。当前的研究旨在通过将现代统计中介分析方法与现代多特征-多方法（MTMM）方法相结合来解决这一问题。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，这些模型具有潜在变量，可以适当地解释研究中使用的方法类型（目标2）。根据Eid等人（2008）的研究，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结合开发模型。模拟研究将用于评价新模型的绝对性能以及与其他已建立的方法的关系。基于Aim 1和Aim 2中模拟研究的发现，我们将把表现最好的MM中介模型应用于真实的预防数据集（Aim 3）。最后，本研究的最终目标是向应用研究人员传播有关如何在多方法测量设计的背景下最适当地分析中介效应的知识（目的4）。成功实现这一项目提出的目标将影响公共卫生，因为它将有助于澄清中介作用在预防中的意义

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.