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

Author:

Joo Seang-Hwane1,Wang Yan2,Ferron John3,Beretvas S. Natasha4,Moeyaert Mariola5,Van Den Noortgate Wim6

Affiliation:

1. The University of Kansas

2. University of Massachusetts Lowell

3. University of South Florida

4. The University of Texas at Austin

5. The State University of New York

6. Katholieke Universiteit Leuven

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.

Funder

Institute of Education Sciences

Publisher

American Educational Research Association (AERA)

Subject

Social Sciences (miscellaneous),Education

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