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Confidence intervals for single-case effect size measures based on randomization test inversion.

Bart Michiels1, Mieke Heyvaert2, Ann Meulders2

  • 1Faculty of Psychology and Educational Sciences, KU Leuven - University of Leuven, Leuven, Belgium. Bart.Michiels@ppw.kuleuven.be.

Behavior Research Methods
|March 2, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces Randomization Test Inversion (RTI), a new method for creating nonparametric confidence intervals (CIs) for single-case effect sizes. RTI offers a flexible approach for analyzing treatment effects in various single-case designs.

Keywords:
Confidence intervalsEffect sizeHypothesis testingNonparametric statisticsRandomization testsSingle-case experiments

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Area of Science:

  • Single-case experimental designs
  • Nonparametric statistics
  • Effect size estimation

Background:

  • Single-case designs are crucial for evaluating interventions in individual subjects.
  • Accurate effect size estimation with confidence intervals is vital for interpreting results.
  • Existing methods for nonparametric confidence intervals in single-case designs have limitations.

Purpose of the Study:

  • To present a novel method for constructing nonparametric confidence intervals (CIs) for single-case effect size measures.
  • To introduce Randomization Test Inversion (RTI) as a practical approach for CI construction.
  • To demonstrate the applicability of RTI across various single-case designs and effect size measures.

Main Methods:

  • Utilized the principle of hypothesis test inversion (HTI).
  • Employed randomization tests as the statistical hypothesis test.
  • Applied the method to unstandardized and standardized mean differences in a completely randomized single-case design.

Main Results:

  • Successfully constructed nonparametric CIs for single-case effect sizes using RTI.
  • Demonstrated the extension of RTI to other single-case designs.
  • Provided R code for practical implementation of the RTI method.

Conclusions:

  • RTI provides a robust and flexible method for generating nonparametric CIs for single-case effect sizes.
  • The method is adaptable to different single-case designs and effect size metrics.
  • RTI facilitates more reliable interpretation of treatment effects in single-case research.