Journal
MULTIVARIATE BEHAVIORAL RESEARCH
Volume 56, Issue 3, Pages 496-513Publisher
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/00273171.2020.1738910
Keywords
Mediation; power; indirect effects; multilevel models; sample size determination; experimental design
Categories
Funding
- National Science Foundation [1437679, 1552535, 1437745, 1437692]
- Division Of Graduate Education
- Direct For Education and Human Resources [1437679, 1437692, 1437745] Funding Source: National Science Foundation
Ask authors/readers for more resources
Mediation analyses provide a crucial method to explore how treatments impact outcomes through pathways. This study offers a method for calculating power to detect mediation effects in three-level cluster-randomized designs and provides examples of application in three-level clinic-randomized studies.
Mediation analyses supply a principal lens to probe the pathways through which a treatment acts upon an outcome because they can dismantle and test the core components of treatments and test how these components function as a coordinated system or theory of action. Experimental evaluation of mediation effects in addition to total effects has become increasingly common but literature has developed only limited guidance on how to plan mediation studies with multi-tiered hierarchical or clustered structures. In this study, we provide methods for computing the power to detect mediation effects in three-level cluster-randomized designs that examine individual- (level one), intermediate- (level two) or cluster-level (level three) mediators. We assess the methods using a simulation and provide examples of a three-level clinic-randomized study (individuals nested within therapists nested within clinics) probing an individual-, intermediate- or cluster-level mediator using the R package PowerUpR and its Shiny application.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available