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This study aims to summarize strategies for researching issues with small sample sizes in rehabilitation. Explore the relevance of underpowered studies, sample size requirements, and recruitment goals. Learn about ideal methods for clinical research. Results reveal advancements in single-case research analysis. Conclusions emphasize the importance of small N studies in rehabilitation research and advocate for educating administrators and reviewers on their value.
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Small N designs for rehabilitation research Scott D. Barnett, PhD; Allen W. Heinemann, PhD; Alexander Libin, PhD; Arthur C. Houts, PhD; Julie Gassaway, MS, RN; Sunil Sen-Gupta, PhD; Aaron Resch, MS; Daniel F. Brossart, PhD
Study Aim • Summarize strategies that rehabilitation researchers may consider for studying issues with expected small sample sizes. • Relevance • Underpowered studies, sample size requirements, and recruitment goals often plague rehabilitation research. • Quasi-experimental and experimental small N designs are ideal methods for clinical research in which understanding and changing maladaptive patterns in behavior and functional status are primary goals.
Methods • January 2010 State-of-the-Art conference: • Department of Veterans Affairs (VA) Rehabilitation Research and Development Service convened conference in Miami, Florida, to discuss current and future seminal issues pertinent to rehabilitation research both with and without VA services.
Results • For single-case research: • Field has moved from visual analysis only to both visual and statistical analysis. • Visual analysis of single-case data graphed over time is no longer sufficient. • All single-case data should be analyzed using both visual and statistical methods.
Conclusions • Small N studies (including pilot studies): • Should be conducted more often. • Are especially important in rehabilitation. • Are specifically important during initial research development and the study of complex behavioral issues associated with co-occurring conditions. • Administrators, grant reviewers, and journal editors should be educated on value of small N studies.