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Area of Science:

  • Behavioral analysis
  • Single-case research design
  • Quantitative methodology

Background:

  • Visual analysis of single-case data commonly assesses level, trend, variability, and effect consistency.
  • Quantifying these data aspects alongside visual inspection is standard practice.
  • Defining and controlling for linear trends in single-case data presents definitional and methodological challenges.

Purpose of the Study:

  • To propose and implement a quantitative approach for analyzing trends in single-case data.
  • To provide researchers with a tool adhering to What Works Clearinghouse Standards for visual analysis.
  • To enhance the objectivity and reproducibility of single-case data analysis.

Main Methods:

  • Focus on trend analysis, recommending quantitative criteria for selecting trend line fitting techniques.
  • Advocates for comparing baseline and intervention slopes directly, rather than detrending.
  • Developed a free, web-based application for visual analysis, incorporating quantifications of level, trend, overlap, and immediate effects.

Main Results:

  • The proposed method offers a quantitative criterion for trend line fitting and phase comparison.
  • The web application provides graphical data representation, visual aids, and effect quantifications.
  • Includes evaluations of effect consistency across replications and detailed formulas for statisticians.

Conclusions:

  • Quantitative trend analysis and direct slope comparison offer a robust alternative to detrending.
  • The developed application supports applied researchers in rigorous single-case data analysis.
  • Enhances reproducibility and adherence to methodological standards in behavioral research.