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This study introduces new data analysis methods for alternating treatments designs (ATDs), a type of single-case experimental design. These novel approaches offer improved comparisons between conditions without requiring specific data patterns.

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Area of Science:

  • Behavioral Science
  • Psychology
  • Research Methodology

Background:

  • Alternating treatments designs (ATDs) are less analyzed than other single-case designs.
  • Existing ATD analysis methods often assume consecutive measurements within conditions.

Purpose of the Study:

  • To review desirable features and characteristics of ATDs relevant to data analysis.
  • To examine current data analysis options for ATDs.
  • To propose and illustrate novel analytical procedures for ATDs.

Main Methods:

  • Review of ATD methodological features and published research.
  • Analysis of existing ATD data analysis techniques.
  • Development and application of two new analytical procedures.
  • Provision of R code for computations and graphical representation.

Main Results:

  • Existing ATD analysis methods have limitations.
  • The proposed new procedures offer meaningful comparisons between conditions.
  • The new methods do not rely on assumptions about the design or data patterns.

Conclusions:

  • Advocacy for the adoption of the new analytical proposals for ATDs.
  • The new methods enhance the analytical rigor of ATDs.
  • The study provides practical tools (R code) for implementing these analyses.