Comparing the Bayesian Unknown Change-Point Model and Simulation Modeling Analysis to Analyze Single Case Experimental Designs

Prathiba Natesan Batley, Ratna Nandakumar, Jayme M. Palka, Pragya Shrestha

Research output: Contribution to journalArticlepeer-review

Abstract

Recently, there has been an increased interest in developing statistical methodologies for analyzing single case experimental design (SCED) data to supplement visual analysis. Some of these are simulation-driven such as Bayesian methods because Bayesian methods can compensate for small sample sizes, which is a main challenge of SCEDs. Two simulation-driven approaches: Bayesian unknown change-point model (BUCP) and simulation modeling analysis (SMA) were compared in the present study for three real datasets that exhibit “clear” immediacy, “unclear” immediacy, and delayed effects. Although SMA estimates can be used to answer some aspects of functional relationship between the independent and the outcome variables, they cannot address immediacy or provide an effect size estimate that considers autocorrelation as required by the What Works Clearinghouse (WWC) Standards. BUCP overcomes these drawbacks of SMA. In final analysis, it is recommended that both visual and statistical analyses be conducted for a thorough analysis of SCEDs.

Original languageEnglish (US)
Article number617047
JournalFrontiers in Psychology
Volume11
DOIs
StatePublished - Jan 15 2021
Externally publishedYes

Keywords

  • Bayesian
  • Markov Chain Monte Carlo Method
  • interrupted time series analysis
  • single case design
  • single case experimental designs
  • statistical simulation model

ASJC Scopus subject areas

  • Psychology(all)

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