Design-Comparable effect sizes in multiple baseline designs: A general modeling framework

James E. Pustejovsky*, Larry V. Hedges, William R. Shadish

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

83 Scopus citations


In single-case research, the multiple baseline design is a widely used approach for evaluating the effects of interventions on individuals. Multiple baseline designs involve repeated measurement of outcomes over time and the controlled introduction of a treatment at different times for different individuals. This article outlines a general framework for defining effect sizes in multiple baseline designs that are directly comparable to the standardized mean difference from a between-subjects randomized experiment. The target, design-comparable effect size parameter can be estimated using restricted maximum likelihood together with a small sample correction analogous to Hedges’s g. The approach is demonstrated using hierarchical linear models that include baseline time trends and treatment-by-time interactions. A simulation compares the performance of the proposed estimator to that of an alternative, and an application illustrates the model-fitting process.

Original languageEnglish (US)
Pages (from-to)368-393
Number of pages26
JournalJournal of Educational and Behavioral Statistics
Issue number5
StatePublished - Oct 13 2014


  • Effect size
  • Hierarchical linear model
  • Single-case research

ASJC Scopus subject areas

  • Education
  • Social Sciences (miscellaneous)


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