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Extrapolating baseline trend in single-case data: Problems and tentative solutions.

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Extrapolating baseline trends in single-case data can yield impossible forecasts. This study introduces a damping trend extrapolation method and criteria to ensure accurate effect size quantification in data analysis.

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Area of Science:

  • Behavioral Science
  • Psychology
  • Educational Research

Background:

  • Single-case data analysis frequently involves baseline trends.
  • Current extrapolation methods can produce impossible forecasts in intervention phases.
  • A significant percentage of reviewed studies (40%) exhibited such impossible values.

Purpose of the Study:

  • To propose a method for extrapolating baseline trends in single-case data that avoids impossible values.
  • To introduce criteria for determining the necessity and extent of trend extrapolation.
  • To improve the accuracy of effect size quantification in single-case research.

Main Methods:

  • Developed a criterion for trend extrapolation based on baseline length and trend line fit.
  • Modified the Mean Phase Difference (MPD) analytical technique to incorporate damping trend extrapolation.
  • Utilized real and generated data to validate the proposed methods.
  • Implemented the new techniques in a user-friendly Shiny application.

Main Results:

  • Unjustified trend extrapolation can lead to inappropriate effect size calculations.
  • The proposed damping trend extrapolation and criteria prevent impossible forecasts.
  • The modified MPD technique provides more accurate effect size estimations.
  • The Shiny application offers accessible graphical and numerical data insights.

Conclusions:

  • The proposed method enhances the reliability of single-case data analysis by preventing impossible trend extrapolations.
  • Accurate trend extrapolation is crucial for valid effect size interpretation.
  • An alternative method not requiring trend fitting or extrapolation is also presented.