Multimethod Mediation Analysis in Prevention Research

说明(申请人提供)：预防领域在很大程度上依赖于理解因果过程，以此作为确定潜在预防目标和干预措施如何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有信息量，并允许评估收敛的有效性和方法特异性。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，但许多在统计分析中整合多种方法的方法在理论和经验上都有很大的局限性。本研究旨在通过将现代统计中介分析方法与现代多特质-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)开发和评估具有潜在变量的新的多方法调解模型，以适当地解释研究中使用的方法的类型(目标2)。与开斋节等人的观点一致。在这方面，我们区分了可互换的方法和结构上不同的方法，并建议为每种方法开发模型以及两者的组合。将使用模拟研究来评估新模型的绝对值以及与其他已经确立的方法的相关性能。基于我们在目标1和目标2中的模拟研究的结果，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(目标3)。最后，这项研究的最终目标是向应用研究人员传播关于如何在多方法测量设计(目标4)的背景下最适当地分析中介效应的知识。本项目提出的目标的成功实现将影响公众健康，因为这将有助于澄清预防中中介作用的含义。 研究，这是设计有效的预防干预措施的关键要素。

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.