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Assessing Consistency in Single-Case A-B-A-B Phase Designs.

René TaniousTamal Kumar DeBart MichielsWim Van den NoortgatePatrick Onghena
Published in: Behavior modification (2019)
Previous research has introduced several effect size measures (ESMs) to quantify data aspects of single-case experimental designs (SCEDs): level, trend, variability, overlap, and immediacy. In the current article, we extend the existing literature by introducing two methods for quantifying consistency in single-case A-B-A-B phase designs. The first method assesses the consistency of data patterns across phases implementing the same condition, called CONsistency of DAta Patterns (CONDAP). The second measure assesses the consistency of the five other data aspects when changing from baseline to experimental phase, called CONsistency of the EFFects (CONEFF). We illustrate the calculation of both measures for four A-B-A-B phase designs from published literature and demonstrate how CONDAP and CONEFF can supplement visual analysis of SCED data. Finally, we discuss directions for future research.
Keyphrases
  • electronic health record
  • big data
  • systematic review
  • machine learning
  • artificial intelligence
  • current status