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Assigning and combining probabilities in single-case studies: a second study.

Rumen Manolov1, Antonio Solanas

  • 1Department of Behavioral Sciences Methods, Faculty of Psychology, University of Barcelona, Barcelona, Spain, rrumenov13@ub.edu.

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|March 26, 2013
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Summary
This summary is machine-generated.

This study proposes assigning probabilities to single-case study outcomes. These probabilities, derived from comparing results to reference values, effectively reflect intervention effectiveness and aid in interpreting behavioral change.

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Area of Science:

  • Behavioral Science
  • Psychometrics
  • Single-Case Experimental Designs

Background:

  • Previous work proposed assigning probabilities to single-case study outcomes.
  • These probabilities indicate the likelihood of observed results assuming no intervention effect.

Purpose of the Study:

  • To evaluate the translation of different primary indicators into p-values using simulation data.
  • To assess how well these probabilities reflect intervention effectiveness in published multiple-baseline studies.
  • To explore the impact of indicator selection and probability combination methods (weighted average, binomial test) on effectiveness assessment.

Main Methods:

  • Simulations were used to test the conversion of various single-case study metrics into p-values.
  • Two published multiple-baseline datasets were analyzed to validate the probability assignments against author-assessed effectiveness.
  • Probabilities were combined using a weighted average and a binomial test to assess overall intervention impact.

Main Results:

  • The translation into p-values performed well for nonoverlap procedures but showed divergence for a regression-based procedure.
  • Individual and combined p-values demonstrated good alignment with intervention effectiveness in real-world data.
  • The choice of primary indicator and combination method influenced the assessment of effectiveness.

Conclusions:

  • Assigning probabilities offers a standardized metric for evaluating single-case study outcomes.
  • These probabilities can serve as supplementary evidence for behavioral change, complementing visual analysis and expert judgment.
  • The proposed method enhances the interpretation of intervention effectiveness in single-case research.