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Threats to Internal Validity in Multiple-Baseline Design Variations.

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Summary
This summary is machine-generated.

Nonconcurrent multiple baseline designs are as rigorous as concurrent designs for applied behavior analysis research. This study argues against the prevailing view that nonconcurrent designs are less rigorous, advocating for their more appropriate use.

Keywords:
ConcurrentInternal validityMultiple baseline designNonconcurrentResearch methodologySingle-case design

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Area of Science:

  • Behavioral Science
  • Research Methodology

Background:

  • Multiple baseline designs are prevalent in applied behavior analysis (ABA) and other fields.
  • Historically, concurrent multiple baseline designs were considered more rigorous than nonconcurrent designs.
  • Current literature often devalues nonconcurrent designs due to perceived limitations in controlling for coincidental events.

Purpose of the Study:

  • To challenge the prevailing view that nonconcurrent multiple baseline designs are less rigorous than concurrent designs.
  • To re-evaluate the methodological strengths and weaknesses of both concurrent and nonconcurrent multiple baseline designs.
  • To provide recommendations for improving the application and evaluation of multiple baseline designs.

Main Methods:

  • Defining multiple baseline designs and threats to internal validity.
  • Reviewing historical and contemporary methodological literature on multiple baseline designs.
  • Analyzing how concurrent and nonconcurrent designs address threats to internal validity.
  • Proposing recommendations for rigorous use and reporting.

Main Results:

  • The emphasis on across-tier comparisons in current methodology is not fully justified.
  • Nonconcurrent designs possess capabilities to address threats to internal validity, similar to concurrent designs.
  • The perceived limitations of nonconcurrent designs are often overstated.

Conclusions:

  • Nonconcurrent multiple baseline designs are methodologically sound and should not be deprecated.
  • A balanced approach considering both within-tier and across-tier comparisons is crucial for robust experimental control.
  • Recommendations are provided for enhancing the rigor of multiple baseline design implementation and evaluation.