Monte Carlo Analyses for Single-Case Experimental Designs: An Untapped Resource for Applied Behavioral Researchers and Practitioners

Jonathan E. Friedel, Alison Cox, Ann Galizio, Melissa Swisher, Megan L. Small, Sofia Perez

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Group-based experimental designs are an outgrowth of the logic of null-hypothesis significance testing and thus, statistical tests are often considered inappropriate for single-case experimental designs. Behavior analysts have recently been more supportive of efforts to include appropriate statistical analysis techniques to evaluate single-case experimental design data. One way that behavior analysts can incorporate statistical analyses into their practices with single-case experimental designs is to use Monte Carlo analyses. These analyses compare experimentally obtained behavioral data to simulated samples of behavioral data to determine the likelihood that the experimentally obtained results occurred due to chance (i.e., a p value). Monte Carlo analyses are more in line with behavior analytic principles than traditional null-hypothesis significance testing. We present an open-source Monte Carlo tool, created in shiny, for behavior analysts who want to use Monte Carlo analyses in addition as part of their data analysis.

Original languageEnglish (US)
Pages (from-to)209-237
Number of pages29
JournalPerspectives on Behavior Science
Volume45
Issue number1
DOIs
StatePublished - Mar 2022

All Science Journal Classification (ASJC) codes

  • Social Psychology
  • Experimental and Cognitive Psychology
  • Clinical Psychology

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