A primer for using multilevel models to meta-analyze single case design data with AB phases

Jessica L. Becraft, John C. Borrero, Shuyan Sun, Anlara A. McKenzie

Research output: Contribution to journalArticlepeer-review

Abstract

Meta-analytic methods provide a way to synthesize data across treatment evaluation studies. However, these well-accepted methods are infrequent with behavior analytic studies. Multilevel models may be a promising method to meta-analyze single-case data. This technical article provides a primer for how to conduct a multilevel model with single-case designs with AB phases using data from the differential-reinforcement-of-low-rate behavior literature. We provide details, recommendations, and considerations for searching for appropriate studies, organizing the data, and conducting the analyses. All data sets are available to allow the reader to follow along with this primer. The purpose of this technical article is to minimally equip behavior analysts to complete a meta-analysis that will summarize a current state of affairs as it relates to the science of behavior analysis and its practice. Moreover, we aim to demonstrate the value of analyses of this sort for behavior analysis.

Original languageEnglish (US)
Pages (from-to)1799-1821
Number of pages23
JournalJournal of applied behavior analysis
Volume53
Issue number3
DOIs
StatePublished - Jul 1 2020

Keywords

  • differential-reinforcement-of-low-rate
  • hierarchical linear modeling
  • meta-analysis
  • multilevel model
  • quantitative review
  • single-case designs

ASJC Scopus subject areas

  • Applied Psychology
  • Sociology and Political Science
  • Philosophy

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