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Assessing Consistency in Single-Case Alternation Designs.

Rumen Manolov1, René Tanious2, Tamal Kumar De2

  • 1Department of Social Psychology and Quantitative Psychology, Faculty of Psychology, University of Barcelona, Barcelona, Spain.

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|May 28, 2020
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Summary
This summary is machine-generated.

This study quantifies consistency in single-case alternation designs, focusing on effect magnitude across blocks. This research aids in statistically evaluating intervention effectiveness beyond visual inspection.

Keywords:
alternating treatments designsanalysis of varianceconsistencyrandomized blockssingle-case design

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Area of Science:

  • Behavioral Science
  • Single-Case Research Design
  • Quantitative Methods

Background:

  • Visual inspection is standard for assessing intervention effects in single-case designs.
  • Quantifying data and effect consistency is an emerging area for robust evaluation.
  • Previous work addressed consistency in various single-case designs.

Purpose of the Study:

  • To extend consistency quantification to single-case alternation designs with block randomization.
  • To propose methods for measuring consistency of superiority, average level, and effect magnitude across blocks.
  • To introduce a variance partitioning approach for quantifying effect magnitude consistency.

Main Methods:

  • Focus on alternation designs with block randomization.
  • Quantification of three consistency types: superiority, average level, and effect magnitude.
  • Variance partitioning to assess intervention, block, and residual effects on consistency.
  • Illustrations with real and simulated data.
  • Development of a user-friendly webpage for implementation.

Main Results:

  • Proposed quantification methods for consistency in alternation designs.
  • Demonstrated application of variance partitioning for effect magnitude consistency.
  • Provided graphical representations to supplement numerical assessments.
  • A freely available tool is developed to facilitate the application of these methods.

Conclusions:

  • Numerical and graphical methods enhance the assessment of consistency in alternation designs.
  • Quantifying consistency provides a more objective evaluation of intervention effects.
  • The developed tool supports researchers in applying these advanced analytical techniques.