Design-Comparable Effect Sizes in Multiple Baseline Designs

James Pustejovsky, Larry V. Hedges and William R. Shadish

Journal of Educational and Behavioral Statistics, 2014, vol. 39, issue 5, 368-393

Abstract: 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.

Keywords: Single-case research; effect size; hierarchical linear model (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:39:y:2014:i:5:p:368-393

DOI: 10.3102/1076998614547577

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