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A standardized mean difference effect size for single case designs.

Larry V Hedges1, James E Pustejovsky1, William R Shadish2

  • 1Northwestern University, Evanston, IL, USA.

Research Synthesis Methods
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PubMed
Summary
This summary is machine-generated.

This study introduces a novel effect size measure for single-case designs, enabling direct comparison with Cohen's d. This enhances the analysis and meta-analysis of treatment effects in various research fields.

Keywords:
autocorrelationeffect sizehierarchical linear modelsingle case designs

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Area of Science:

  • Behavior analysis
  • Clinical psychology
  • Special education
  • Medicine

Background:

  • Single-case designs evaluate treatment effects by applying different interventions to the same individual over time.
  • There is a growing need for standardized effect size measures in single-case research for robust summarization and meta-analysis.
  • Current effect size measures lack consensus, hindering consistent application.

Purpose of the Study:

  • To propose a new effect size measure for single-case research.
  • To ensure the new measure is directly comparable to the standardized mean difference (Cohen's d) used in between-subjects designs.
  • To provide methods for estimating the effect size and its variance from treatment reversal designs.

Main Methods:

  • Development of a novel effect size calculation for single-case designs.
  • Techniques for estimating the effect size and its variance from balanced and unbalanced treatment reversal designs.
  • Evaluation of estimation methods through a simulation study and practical applications.

Main Results:

  • A new, comparable effect size measure for single-case research has been proposed.
  • Methods for estimating this effect size and its variance from treatment reversal designs were developed and validated.
  • The proposed methods were demonstrated through simulation and real-world case studies.

Conclusions:

  • The new effect size measure facilitates consistent comparison with between-subjects effect sizes.
  • The provided estimation techniques offer practical tools for researchers using single-case designs.
  • This work addresses the need for consensus on effect size measures in single-case research.