An alternative approach to relapse analysis: Using Monte Carlo methods and proportional rates of response

Jonathan E. Friedel, Ann Galizio, Meredith S. Berry, Mary M. Sweeney, Amy L. Odum

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Relapse is the recovery of a previously suppressed response. Animal models have been useful in examining the mechanisms underlying relapse (e.g., reinstatement, renewal, reacquisition, resurgence). However, there are several challenges to analyzing relapse data using traditional approaches. For example, null hypothesis significance testing is commonly used to determine whether relapse has occurred. However, this method requires several a priori assumptions about the data, as well as a large sample size for between-subjects comparisons or repeated testing for within-subjects comparisons. Monte Carlo methods may represent an improved analytic technique, because these methods require no prior assumptions, permit smaller sample sizes, and can be tailored to account for all of the data from an experiment instead of some limited set. In the present study, we conducted reanalyses of three studies of relapse (Berry, Sweeney, & Odum,; Galizio et al.,; Odum & Shahan,) using Monte Carlo techniques to determine if relapse occurred and if there were differences in rate of response based on relevant independent variables (such as group membership or schedule of reinforcement). These reanalyses supported the previous findings. Finally, we provide general recommendations for using Monte Carlo methods in studies of relapse.

Original languageEnglish (US)
Pages (from-to)289-308
Number of pages20
JournalJournal of the experimental analysis of behavior
Issue number2
StatePublished - Mar 2019


  • Monte Carlo
  • bootstrapping
  • null hypothesis significance testing
  • reinstatement
  • relapse
  • renewal

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Behavioral Neuroscience


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