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Reproducibility in Small-N Treatment Research: A Tutorial Using Examples From Aphasiology.

Robert Cavanaugh1,2, Yina M Quique3, Alexander M Swiderski1,2,4

  • 1Department of Communication Science and Disorders, University of Pittsburgh, PA.

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This tutorial guides researchers in communication sciences and disorders on conducting reproducible analyses and selecting effect sizes for small-N studies, enhancing scientific rigor in aphasia treatment research.

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Area of Science:

  • Communication Sciences and Disorders
  • Aphasia Research
  • Evidence-Based Interventions

Background:

  • Small-N studies are crucial for evidence-based interventions in communication science and disorders, particularly for aphasia treatments.
  • Lack of guidance on reproducible analyses and effect size selection in small-N studies hinders scientific review, rigor, and replication.
  • Understanding effect sizes is vital for interpreting and synthesizing research findings in aphasiology.

Purpose of the Study:

  • To demonstrate reproducible analysis methods for small-N studies using common effect sizes in aphasia research.
  • To provide a conceptual understanding of various effect sizes relevant to communication disorders research.
  • To enhance the rigor and replicability of small-N treatment research in aphasiology.

Main Methods:

  • Tutorial utilizing the statistical programming language R with published data from Wambaugh et al. (2017).
  • Demonstration of reproducible analysis for within-case standardized mean difference, proportion of maximal gain, tau-U, and mixed-effects models (frequentist and Bayesian).
  • Discussion of strengths, weaknesses, reporting requirements, and design impacts on effect sizes.

Main Results:

  • Reproducible code provided for calculating and comparing multiple effect sizes.
  • Implementation of frequentist and Bayesian mixed-effects models for small-N data analysis.
  • Availability of data, code, and an interactive web application for researchers, clinicians, and students.

Conclusions:

  • Reproducible research practices are essential for transparency in small-N treatment studies.
  • Informed selection and interpretation of effect sizes improve the quality of small-N research.
  • Commitment to reproducibility and understanding effect sizes can advance the evidence base for clinical services in communication sciences and disorders.