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Competing stimulus assessments: A systematic review.

Jennifer N Haddock1,2, Louis P Hagopian1,2,3

  • 1The Kennedy Krieger Institute.

Journal of Applied Behavior Analysis
|July 29, 2020
PubMed
Summary
This summary is machine-generated.

Competing stimulus assessments (CSAs) identify stimuli that reduce problem behavior (PB). These assessments are broadly useful across PB types and reliably predict which stimuli will be effective in reducing PB long-term.

Keywords:
competing stimulicompeting stimulus assessmentsproblem behaviorreview

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Area of Science:

  • Behavioral science
  • Applied behavior analysis

Background:

  • Problem behavior (PB) is a significant challenge in various settings.
  • Competing stimulus assessments (CSAs) are pretreatment strategies to identify potential interventions.
  • CSAs aim to identify stimuli that reduce PB through reinforcer competition or substitution.

Purpose of the Study:

  • To review and summarize the existing literature on CSAs.
  • To analyze participant characteristics, outcomes, and predictive validity of published CSAs.
  • To evaluate the utility of CSAs across different problem behaviors and functions.

Main Methods:

  • Systematic literature review of studies employing CSAs.
  • Inclusion criteria: studies with no-stimulus control, test trials with single stimulus availability, and measurement of PB and stimulus engagement.
  • Analysis of participant demographics, PB topographies and functions, and CSA outcomes.

Main Results:

  • CSAs demonstrate broad utility across diverse problem behavior topographies and functions.
  • Stimuli identified as effective in reducing PB during CSAs consistently showed efficacy in extended analyses.
  • Predictive validity of CSAs was supported across various conditions, irrespective of stimulus-PB matching.

Conclusions:

  • CSAs are a valuable tool for identifying effective interventions for problem behavior.
  • The findings support the use of CSAs for predicting long-term treatment outcomes.
  • Further research can refine CSA methodologies and expand their application.