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LATENT VARIABLE ANALYSIS (LAVA) ABOUT THE LAB

FUNDING

Currently Funded Grants

2013-2016 1 R01 DA034770-01 - Multimethod Mediation Analysis in Prevention Research. R01 grant funded by the National Institutes of Health (NIH-NIDA). Total costs: $676,424. Role: Principle Investigator together with Ginger Lockhart.

Summary of Project Purpose

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 for 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. In the current project, we aim to address this issue by integrating modern methods of statistical mediation analysis with modern approaches of multitrait-multimethod (MTMM) methodology. In particular, we examine the statistical performance of approaches currently used by prevention scientists and develop new multimethod mediation models with latent variables that properly account for the types of methods (e.g., interchangeable vs. structurally different raters) used in the study. In line with Eid et al. (2008), we develop models for both interchangeable and structurally different methods as well as the combination of both types of methods. We use simulation studies to evaluate the performance of the new models in absolute terms as well as in relation to already established approaches. 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 multimethod/multirater measurement designs.