Comparison of Within- and Between-Series Effect Estimates in the Meta-Analysis of Multiple Baseline Studies

Seang-Hwane Joo, Yan Wang, John Ferron, S. Natasha Beretvas, Mariola Moeyaert and Wim Van Den Noortgate
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Seang-Hwane Joo: 4202The University of Kansas
Yan Wang: 14710University of Massachusetts Lowell
John Ferron: 7831University of South Florida
S. Natasha Beretvas: 441903The University of Texas at Austin
Mariola Moeyaert: The State University of New York
Wim Van Den Noortgate: 26657Katholieke Universiteit Leuven

Journal of Educational and Behavioral Statistics, 2022, vol. 47, issue 2, 131-166

Abstract: Multiple baseline (MB) designs are becoming more prevalent in educational and behavioral research, and as they do, there is growing interest in combining effect size estimates across studies. To further refine the meta-analytic methods of estimating the effect, this study developed and compared eight alternative methods of estimating intervention effects from a set of MB studies. The methods differed in the assumptions made and varied in whether they relied on within- or between-series comparisons, modeled raw data or effect sizes, and did or did not standardize. Small sample functioning was examined through two simulation studies, which showed that when data were consistent with assumptions the bias was consistently less than 5% of the effect size for each method, whereas root mean squared error varied substantially across methods. When assumptions were violated, substantial biases were found. Implications and limitations are discussed.

Keywords: multiple baseline design; single-case; mixed linear model; meta-analysis; standardized effect size (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:47:y:2022:i:2:p:131-166

DOI: 10.3102/10769986211035507

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