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The Compound Multiple-Baseline Design.

Lindsay A Lloveras1,2, Savannah A Tate3,4, Timothy R Vollmer5

  • 1Department of Psychiatry, University of Florida College of Medicine, Gainesville, FL USA.

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Summary
This summary is machine-generated.

The modified multiple-baseline design enhances experimental control in behavior analysis. Staggering baselines across dimensions like individuals and settings mitigates threats to validity from trending baseline data.

Keywords:
Compound multiple baselineExperimental controlExperimental designMultiple baseline design

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Area of Science:

  • Applied Behavior Analysis
  • Research Methodology

Background:

  • The multiple-baseline design is a cornerstone of applied behavior-analytic research.
  • A limitation arises when pre-determined baseline lengths coincide with trending data, potentially confounding results and weakening experimental control.
  • Trending baseline data can mimic treatment effects, compromising the integrity of the multiple-baseline design.

Purpose of the Study:

  • To explore the historical development of modifying the multiple-baseline design.
  • To review contemporary applications of this modified design.
  • To identify and suggest potential new areas for its application in research.

Main Methods:

  • Modification of the traditional multiple-baseline design by staggering baselines across multiple dimensions (e.g., participants, settings, behaviors).
  • Analysis of historical precedents and recent research employing this staggered-baseline approach.
  • Conceptualization of future research applications.

Main Results:

  • The modified design, staggering baselines across dimensions, offers a partial solution to the threat of trending baseline data.
  • This approach strengthens experimental control by reducing the likelihood of implementing the independent variable during unfavorable baseline trends.
  • The article highlights the adaptability and utility of this design modification.

Conclusions:

  • Modifying the multiple-baseline design by staggering across dimensions is a valuable strategy to enhance experimental rigor in behavior analysis.
  • This methodological refinement addresses a critical limitation of the traditional design, particularly concerning baseline data trends.
  • The approach is versatile and holds promise for broader application in diverse research contexts.