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The Assessment of Consistency in Single-Case Experiments: Beyond A-B-A-B Designs.

René TaniousRumen ManolovPatrick Onghena
Published in: Behavior modification (2019)
Quality standards for single-case experimental designs (SCEDs) recommend inspecting six data aspects: level, trend, variability, overlap, immediacy, and consistency of data patterns. The data aspect consistency has long been neglected by visual and statistical analysts of SCEDs despite its importance for inferring a causal relationship. However, recently a first quantification has been proposed in the context of A-B-A-B designs, called CONsistency of DAta Patterns (CONDAP). In the current paper, we extend the existing CONDAP measure for assessing consistency in designs with more than two successive A-B elements (e.g., A-B-A-B-A-B), multiple baseline designs, and changing criterion designs. We illustrate each quantification with published research.
Keyphrases
  • electronic health record
  • big data
  • finite element analysis
  • systematic review
  • data analysis
  • randomized controlled trial
  • machine learning
  • artificial intelligence
  • deep learning