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A multiple randomization testing procedure for level, trend, variability, overlap, immediacy, and consistency in

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Summary

This study introduces a new method for analyzing single-case ABAB designs, enhancing statistical inference for intervention effects. The approach uses multiple randomization tests to assess various data aspects, offering detailed insights beyond visual analysis.

Keywords:
ABAB phase designEffect size measuresFalse discovery rateRandomization testsSingle-case experimental designsVisual analysis

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Area of Science:

  • Behavioral Science
  • Single-Case Research Design
  • Statistical Inference

Background:

  • Single-case research designs (SCRDs) are crucial for evaluating interventions.
  • Traditional visual analysis of SCRDs can be subjective.
  • Objective statistical methods are needed for robust inference in SCRDs.

Purpose of the Study:

  • To present a novel approach for drawing multiple inferences from single-case ABAB designs.
  • To detail the calculation of effect sizes for various data aspects.
  • To integrate effect sizes into multiple randomization tests for robust statistical analysis.

Main Methods:

  • Calculating effect size measures for level, trend, variability, overlap, immediacy, and consistency.
  • Employing multiple randomization tests with calculated effect sizes as test statistics.
  • Implementing false discovery rate (FDR) control using Benjamini-Hochberg and Benjamini-Yekutieli corrections.

Main Results:

  • The multiple randomization testing procedure provides detailed information on intervention effects across data aspects.
  • Application to a published dataset demonstrated the approach's utility.
  • Results offered more granular insights compared to traditional visual analysis.

Conclusions:

  • The proposed method enhances the statistical power and objectivity of single-case ABAB design analysis.
  • It offers a comprehensive framework for assessing intervention effectiveness.
  • Generic R-code is provided to facilitate the application of these analyses.