Maybe Small Is Too Small a Term: Introduction to Advancing Small Sample Prevention Science

Carlotta Ching Ting Fok, David Henry, James Allen

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Prevention research addressing health disparities often involves work with small population groups experiencing such disparities. The goals of this special section are to (1) address the question of what constitutes a small sample; (2) identify some of the key research design and analytic issues that arise in prevention research with small samples; (3) develop applied, problem-oriented, and methodologically innovative solutions to these design and analytic issues; and (4) evaluate the potential role of these innovative solutions in describing phenomena, testing theory, and evaluating interventions in prevention research. Through these efforts, we hope to promote broader application of these methodological innovations. We also seek whenever possible, to explore their implications in more general problems that appear in research with small samples but concern all areas of prevention research. This special section includes two sections. The first section aims to provide input for researchers at the design phase, while the second focuses on analysis. Each article describes an innovative solution to one or more challenges posed by the analysis of small samples, with special emphasis on testing for intervention effects in prevention research. A concluding article summarizes some of their broader implications, along with conclusions regarding future directions in research with small samples in prevention science. Finally, a commentary provides the perspective of the federal agencies that sponsored the conference that gave rise to this special section.

Original languageEnglish (US)
Pages (from-to)943-949
Number of pages7
JournalPrevention Science
Volume16
Issue number7
DOIs
StatePublished - Oct 29 2015

Keywords

  • Ethnic minority research
  • Health disparities
  • Research methods
  • Small samples
  • Statistical methods

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