title: Maximizing the Yield of Small Samples in Prevention Research: A Review of General Strategies and Best Practices.
abstract: The goal of this manuscript is to describe strategies for maximizing the yield of data from small samples in prevention research. We begin by discussing what "small" means as a description of sample size in prevention research. We then present a series of practical strategies for getting the most out of data when sample size is small and constrained. Our focus is the prototypic between-group test for intervention effects; however, we touch on the circumstance in which intervention effects are qualified by one or more moderators. We conclude by highlighting the potential usefulness of graphical methods when sample size is too small for inferential statistical methods.
authors.affiliation: Department of Psychology and Neuroscience, Duke University, Durham, NC, 27708, USA.
authors.forename: Cameron R
authors.initials: CR
authors.lastname: Hopkin
authors.affiliation: Department of Psychology and Neuroscience, Duke University, Durham, NC, 27708, USA. rhoyle@duke.edu.
authors.forename: Rick H
authors.initials: RH
authors.lastname: Hoyle
authors.affiliation: Center for Developmental Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
authors.forename: Nisha C
authors.initials: NC
authors.lastname: Gottfredson
mesh_terms: D006302:Health Services Research; D011314:Preventive Health Services / Q000458:organization & administration*; D018401:Sample Size
keywords: Graphical methods; Maximizing statistical power; Small samples