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Small N designs for rehabilitation research

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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 ... – PowerPoint PPT presentation

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Title: Small N designs for rehabilitation research


1
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

2
  • 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.

3
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.

4
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.

5
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.
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