Design-Comparable Effect Sizes in Multiple Baseline Designs: A General Modeling Framework

Autor/inn/en	Pustejovsky, James E.; Hedges, Larry V.; Shadish, William R.
Titel	Design-Comparable Effect Sizes in Multiple Baseline Designs: A General Modeling Framework
Quelle	In: Journal of Educational and Behavioral Statistics, 39 (2014) 5, S.368-393 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998614547577
Schlagwörter	Hierarchical Linear Modeling; Effect Size; Maximum Likelihood Statistics; Computation; Research Design; Simulation; Comparative Analysis; Goodness of Fit; Statistical Analysis; Meta Analysis; Outcome Measures + Suchen Sie Ihr Suchwort? Forschungsdesign; Simulation program; Simulationsprogramm; Statistische Analyse; Meta-analysis; Metaanalyse
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10