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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.
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James E. Pustejovsky
Larry V. Hedges
William R. Shadish
Journal of Educational and Behavioral Statistics
The University of Texas at Austin
University of California System
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Pustejovsky et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69dbbf5750e1971baba3c67a — DOI: https://doi.org/10.3102/1076998614547577
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